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Politics and privacy, private-sector influence and big tech, state competition and conflict, author biography, how is technology changing the world, and how should the world change technology.

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Josephine Wolff; How Is Technology Changing the World, and How Should the World Change Technology?. Global Perspectives 1 February 2021; 2 (1): 27353. doi: https://doi.org/10.1525/gp.2021.27353

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Technologies are becoming increasingly complicated and increasingly interconnected. Cars, airplanes, medical devices, financial transactions, and electricity systems all rely on more computer software than they ever have before, making them seem both harder to understand and, in some cases, harder to control. Government and corporate surveillance of individuals and information processing relies largely on digital technologies and artificial intelligence, and therefore involves less human-to-human contact than ever before and more opportunities for biases to be embedded and codified in our technological systems in ways we may not even be able to identify or recognize. Bioengineering advances are opening up new terrain for challenging philosophical, political, and economic questions regarding human-natural relations. Additionally, the management of these large and small devices and systems is increasingly done through the cloud, so that control over them is both very remote and removed from direct human or social control. The study of how to make technologies like artificial intelligence or the Internet of Things “explainable” has become its own area of research because it is so difficult to understand how they work or what is at fault when something goes wrong (Gunning and Aha 2019) .

This growing complexity makes it more difficult than ever—and more imperative than ever—for scholars to probe how technological advancements are altering life around the world in both positive and negative ways and what social, political, and legal tools are needed to help shape the development and design of technology in beneficial directions. This can seem like an impossible task in light of the rapid pace of technological change and the sense that its continued advancement is inevitable, but many countries around the world are only just beginning to take significant steps toward regulating computer technologies and are still in the process of radically rethinking the rules governing global data flows and exchange of technology across borders.

These are exciting times not just for technological development but also for technology policy—our technologies may be more advanced and complicated than ever but so, too, are our understandings of how they can best be leveraged, protected, and even constrained. The structures of technological systems as determined largely by government and institutional policies and those structures have tremendous implications for social organization and agency, ranging from open source, open systems that are highly distributed and decentralized, to those that are tightly controlled and closed, structured according to stricter and more hierarchical models. And just as our understanding of the governance of technology is developing in new and interesting ways, so, too, is our understanding of the social, cultural, environmental, and political dimensions of emerging technologies. We are realizing both the challenges and the importance of mapping out the full range of ways that technology is changing our society, what we want those changes to look like, and what tools we have to try to influence and guide those shifts.

Technology can be a source of tremendous optimism. It can help overcome some of the greatest challenges our society faces, including climate change, famine, and disease. For those who believe in the power of innovation and the promise of creative destruction to advance economic development and lead to better quality of life, technology is a vital economic driver (Schumpeter 1942) . But it can also be a tool of tremendous fear and oppression, embedding biases in automated decision-making processes and information-processing algorithms, exacerbating economic and social inequalities within and between countries to a staggering degree, or creating new weapons and avenues for attack unlike any we have had to face in the past. Scholars have even contended that the emergence of the term technology in the nineteenth and twentieth centuries marked a shift from viewing individual pieces of machinery as a means to achieving political and social progress to the more dangerous, or hazardous, view that larger-scale, more complex technological systems were a semiautonomous form of progress in and of themselves (Marx 2010) . More recently, technologists have sharply criticized what they view as a wave of new Luddites, people intent on slowing the development of technology and turning back the clock on innovation as a means of mitigating the societal impacts of technological change (Marlowe 1970) .

At the heart of fights over new technologies and their resulting global changes are often two conflicting visions of technology: a fundamentally optimistic one that believes humans use it as a tool to achieve greater goals, and a fundamentally pessimistic one that holds that technological systems have reached a point beyond our control. Technology philosophers have argued that neither of these views is wholly accurate and that a purely optimistic or pessimistic view of technology is insufficient to capture the nuances and complexity of our relationship to technology (Oberdiek and Tiles 1995) . Understanding technology and how we can make better decisions about designing, deploying, and refining it requires capturing that nuance and complexity through in-depth analysis of the impacts of different technological advancements and the ways they have played out in all their complicated and controversial messiness across the world.

These impacts are often unpredictable as technologies are adopted in new contexts and come to be used in ways that sometimes diverge significantly from the use cases envisioned by their designers. The internet, designed to help transmit information between computer networks, became a crucial vehicle for commerce, introducing unexpected avenues for crime and financial fraud. Social media platforms like Facebook and Twitter, designed to connect friends and families through sharing photographs and life updates, became focal points of election controversies and political influence. Cryptocurrencies, originally intended as a means of decentralized digital cash, have become a significant environmental hazard as more and more computing resources are devoted to mining these forms of virtual money. One of the crucial challenges in this area is therefore recognizing, documenting, and even anticipating some of these unexpected consequences and providing mechanisms to technologists for how to think through the impacts of their work, as well as possible other paths to different outcomes (Verbeek 2006) . And just as technological innovations can cause unexpected harm, they can also bring about extraordinary benefits—new vaccines and medicines to address global pandemics and save thousands of lives, new sources of energy that can drastically reduce emissions and help combat climate change, new modes of education that can reach people who would otherwise have no access to schooling. Regulating technology therefore requires a careful balance of mitigating risks without overly restricting potentially beneficial innovations.

Nations around the world have taken very different approaches to governing emerging technologies and have adopted a range of different technologies themselves in pursuit of more modern governance structures and processes (Braman 2009) . In Europe, the precautionary principle has guided much more anticipatory regulation aimed at addressing the risks presented by technologies even before they are fully realized. For instance, the European Union’s General Data Protection Regulation focuses on the responsibilities of data controllers and processors to provide individuals with access to their data and information about how that data is being used not just as a means of addressing existing security and privacy threats, such as data breaches, but also to protect against future developments and uses of that data for artificial intelligence and automated decision-making purposes. In Germany, Technische Überwachungsvereine, or TÜVs, perform regular tests and inspections of technological systems to assess and minimize risks over time, as the tech landscape evolves. In the United States, by contrast, there is much greater reliance on litigation and liability regimes to address safety and security failings after-the-fact. These different approaches reflect not just the different legal and regulatory mechanisms and philosophies of different nations but also the different ways those nations prioritize rapid development of the technology industry versus safety, security, and individual control. Typically, governance innovations move much more slowly than technological innovations, and regulations can lag years, or even decades, behind the technologies they aim to govern.

In addition to this varied set of national regulatory approaches, a variety of international and nongovernmental organizations also contribute to the process of developing standards, rules, and norms for new technologies, including the International Organization for Standardization­ and the International Telecommunication Union. These multilateral and NGO actors play an especially important role in trying to define appropriate boundaries for the use of new technologies by governments as instruments of control for the state.

At the same time that policymakers are under scrutiny both for their decisions about how to regulate technology as well as their decisions about how and when to adopt technologies like facial recognition themselves, technology firms and designers have also come under increasing criticism. Growing recognition that the design of technologies can have far-reaching social and political implications means that there is more pressure on technologists to take into consideration the consequences of their decisions early on in the design process (Vincenti 1993; Winner 1980) . The question of how technologists should incorporate these social dimensions into their design and development processes is an old one, and debate on these issues dates back to the 1970s, but it remains an urgent and often overlooked part of the puzzle because so many of the supposedly systematic mechanisms for assessing the impacts of new technologies in both the private and public sectors are primarily bureaucratic, symbolic processes rather than carrying any real weight or influence.

Technologists are often ill-equipped or unwilling to respond to the sorts of social problems that their creations have—often unwittingly—exacerbated, and instead point to governments and lawmakers to address those problems (Zuckerberg 2019) . But governments often have few incentives to engage in this area. This is because setting clear standards and rules for an ever-evolving technological landscape can be extremely challenging, because enforcement of those rules can be a significant undertaking requiring considerable expertise, and because the tech sector is a major source of jobs and revenue for many countries that may fear losing those benefits if they constrain companies too much. This indicates not just a need for clearer incentives and better policies for both private- and public-sector entities but also a need for new mechanisms whereby the technology development and design process can be influenced and assessed by people with a wider range of experiences and expertise. If we want technologies to be designed with an eye to their impacts, who is responsible for predicting, measuring, and mitigating those impacts throughout the design process? Involving policymakers in that process in a more meaningful way will also require training them to have the analytic and technical capacity to more fully engage with technologists and understand more fully the implications of their decisions.

At the same time that tech companies seem unwilling or unable to rein in their creations, many also fear they wield too much power, in some cases all but replacing governments and international organizations in their ability to make decisions that affect millions of people worldwide and control access to information, platforms, and audiences (Kilovaty 2020) . Regulators around the world have begun considering whether some of these companies have become so powerful that they violate the tenets of antitrust laws, but it can be difficult for governments to identify exactly what those violations are, especially in the context of an industry where the largest players often provide their customers with free services. And the platforms and services developed by tech companies are often wielded most powerfully and dangerously not directly by their private-sector creators and operators but instead by states themselves for widespread misinformation campaigns that serve political purposes (Nye 2018) .

Since the largest private entities in the tech sector operate in many countries, they are often better poised to implement global changes to the technological ecosystem than individual states or regulatory bodies, creating new challenges to existing governance structures and hierarchies. Just as it can be challenging to provide oversight for government use of technologies, so, too, oversight of the biggest tech companies, which have more resources, reach, and power than many nations, can prove to be a daunting task. The rise of network forms of organization and the growing gig economy have added to these challenges, making it even harder for regulators to fully address the breadth of these companies’ operations (Powell 1990) . The private-public partnerships that have emerged around energy, transportation, medical, and cyber technologies further complicate this picture, blurring the line between the public and private sectors and raising critical questions about the role of each in providing critical infrastructure, health care, and security. How can and should private tech companies operating in these different sectors be governed, and what types of influence do they exert over regulators? How feasible are different policy proposals aimed at technological innovation, and what potential unintended consequences might they have?

Conflict between countries has also spilled over significantly into the private sector in recent years, most notably in the case of tensions between the United States and China over which technologies developed in each country will be permitted by the other and which will be purchased by other customers, outside those two countries. Countries competing to develop the best technology is not a new phenomenon, but the current conflicts have major international ramifications and will influence the infrastructure that is installed and used around the world for years to come. Untangling the different factors that feed into these tussles as well as whom they benefit and whom they leave at a disadvantage is crucial for understanding how governments can most effectively foster technological innovation and invention domestically as well as the global consequences of those efforts. As much of the world is forced to choose between buying technology from the United States or from China, how should we understand the long-term impacts of those choices and the options available to people in countries without robust domestic tech industries? Does the global spread of technologies help fuel further innovation in countries with smaller tech markets, or does it reinforce the dominance of the states that are already most prominent in this sector? How can research universities maintain global collaborations and research communities in light of these national competitions, and what role does government research and development spending play in fostering innovation within its own borders and worldwide? How should intellectual property protections evolve to meet the demands of the technology industry, and how can those protections be enforced globally?

These conflicts between countries sometimes appear to challenge the feasibility of truly global technologies and networks that operate across all countries through standardized protocols and design features. Organizations like the International Organization for Standardization, the World Intellectual Property Organization, the United Nations Industrial Development Organization, and many others have tried to harmonize these policies and protocols across different countries for years, but have met with limited success when it comes to resolving the issues of greatest tension and disagreement among nations. For technology to operate in a global environment, there is a need for a much greater degree of coordination among countries and the development of common standards and norms, but governments continue to struggle to agree not just on those norms themselves but even the appropriate venue and processes for developing them. Without greater global cooperation, is it possible to maintain a global network like the internet or to promote the spread of new technologies around the world to address challenges of sustainability? What might help incentivize that cooperation moving forward, and what could new structures and process for governance of global technologies look like? Why has the tech industry’s self-regulation culture persisted? Do the same traditional drivers for public policy, such as politics of harmonization and path dependency in policy-making, still sufficiently explain policy outcomes in this space? As new technologies and their applications spread across the globe in uneven ways, how and when do they create forces of change from unexpected places?

These are some of the questions that we hope to address in the Technology and Global Change section through articles that tackle new dimensions of the global landscape of designing, developing, deploying, and assessing new technologies to address major challenges the world faces. Understanding these processes requires synthesizing knowledge from a range of different fields, including sociology, political science, economics, and history, as well as technical fields such as engineering, climate science, and computer science. A crucial part of understanding how technology has created global change and, in turn, how global changes have influenced the development of new technologies is understanding the technologies themselves in all their richness and complexity—how they work, the limits of what they can do, what they were designed to do, how they are actually used. Just as technologies themselves are becoming more complicated, so are their embeddings and relationships to the larger social, political, and legal contexts in which they exist. Scholars across all disciplines are encouraged to join us in untangling those complexities.

Josephine Wolff is an associate professor of cybersecurity policy at the Fletcher School of Law and Diplomacy at Tufts University. Her book You’ll See This Message When It Is Too Late: The Legal and Economic Aftermath of Cybersecurity Breaches was published by MIT Press in 2018.

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• Review article
• Open access
• Published: 02 October 2017

Computer-based technology and student engagement: a critical review of the literature

• Laura A. Schindler   ORCID: orcid.org/0000-0001-8730-5189 1 ,
• Gary J. Burkholder 2 , 3 ,
• Osama A. Morad 1 &
• Craig Marsh 4  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  25 ( 2017 ) Cite this article

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Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites ( Facebook and Twitter ), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook . Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement.

Introduction

The digital revolution has profoundly affected daily living, evident in the ubiquity of mobile devices and the seamless integration of technology into common tasks such as shopping, reading, and finding directions (Anderson, 2016 ; Smith & Anderson, 2016 ; Zickuhr & Raine, 2014 ). The use of computers, mobile devices, and the Internet is at its highest level to date and expected to continue to increase as technology becomes more accessible, particularly for users in developing countries (Poushter, 2016 ). In addition, there is a growing number of people who are smartphone dependent, relying solely on smartphones for Internet access (Anderson & Horrigan, 2016 ) rather than more expensive devices such as laptops and tablets. Greater access to and demand for technology has presented unique opportunities and challenges for many industries, some of which have thrived by effectively digitizing their operations and services (e.g., finance, media) and others that have struggled to keep up with the pace of technological innovation (e.g., education, healthcare) (Gandhi, Khanna, & Ramaswamy, 2016 ).

Integrating technology into teaching and learning is not a new challenge for universities. Since the 1900s, administrators and faculty have grappled with how to effectively use technical innovations such as video and audio recordings, email, and teleconferencing to augment or replace traditional instructional delivery methods (Kaware & Sain, 2015 ; Westera, 2015 ). Within the past two decades, however, this challenge has been much more difficult due to the sheer volume of new technologies on the market. For example, in the span of 7 years (from 2008 to 2015), the number of active apps in Apple’s App Store increased from 5000 to 1.75 million. Over the next 4 years, the number of apps is projected to rise by 73%, totaling over 5 million (Nelson, 2016 ). Further compounding this challenge is the limited shelf life of new devices and software combined with significant internal organizational barriers that hinder universities from efficiently and effectively integrating new technologies (Amirault, 2012 ; Kinchin, 2012 ; Linder-VanBerschot & Summers 2015 ; Westera, 2015 ).

Many organizational barriers to technology integration arise from competing tensions between institutional policy and practice and faculty beliefs and abilities. For example, university administrators may view technology as a tool to attract and retain students, whereas faculty may struggle to determine how technology coincides with existing pedagogy (Lawrence & Lentle-Keenan, 2013 ; Lin, Singer, & Ha, 2010 ). In addition, some faculty may be hesitant to use technology due to lack of technical knowledge and/or skepticism about the efficacy of technology to improve student learning outcomes (Ashrafzadeh & Sayadian, 2015 ; Buchanan, Sainter, & Saunders, 2013 ; Hauptman, 2015 ; Johnson, 2013 ; Kidd, Davis, & Larke, 2016 ; Kopcha, Rieber, & Walker, 2016 ; Lawrence & Lentle-Keenan, 2013 ; Lewis, Fretwell, Ryan, & Parham, 2013 ; Reid, 2014 ). Organizational barriers to technology adoption are particularly problematic given the growing demands and perceived benefits among students about using technology to learn (Amirault, 2012 ; Cassidy et al., 2014 ; Gikas & Grant, 2013 ; Paul & Cochran, 2013 ). Surveys suggest that two-thirds of students use mobile devices for learning and believe that technology can help them achieve learning outcomes and better prepare them for a workforce that is increasingly dependent on technology (Chen, Seilhamer, Bennett, & Bauer, 2015 ; Dahlstrom, 2012 ). Universities that fail to effectively integrate technology into the learning experience miss opportunities to improve student outcomes and meet the expectations of a student body that has grown accustomed to the integration of technology into every facet of life (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ).

The purpose of this paper is to provide a literature review on how computer-based technology influences student engagement within higher education settings. We focused on computer-based technology given the specific types of technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) that emerged from a broad search of the literature, which is described in more detail below. Computer-based technology (hereafter referred to as technology) requires the use of specific hardware, software, and micro processing features available on a computer or mobile device. We also focused on student engagement as the dependent variable of interest because it encompasses many different aspects of the teaching and learning process (Bryson & Hand, 2007 ; Fredricks, Blumenfeld, & Parks, 1994; Wimpenny & Savin-Baden, 2013 ), compared narrower variables in the literature such as final grades or exam scores. Furthermore, student engagement has received significant attention over the past several decades due to shifts towards student-centered, constructivist instructional methods (Haggis, 2009 ; Wright, 2011 ), mounting pressures to improve teaching and learning outcomes (Axelson & Flick, 2011 ; Kuh, 2009 ), and promising studies suggesting relationships between student engagement and positive academic outcomes (Carini, Kuh, & Klein, 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). Despite the interest in student engagement and the demand for more technology in higher education, there are no articles offering a comprehensive review of how these two variables intersect. Similarly, while many existing student engagement conceptual models have expanded to include factors that influence student engagement, none highlight the overt role of technology in the engagement process (Kahu, 2013 ; Lam, Wong, Yang, & Yi, 2012 ; Nora, Barlow, & Crisp, 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ).

Our review aims to address existing gaps in the student engagement literature and seeks to determine whether student engagement models should be expanded to include technology. The review also addresses some of the organizational barriers to technology integration (e.g., faculty uncertainty and skepticism about technology) by providing a comprehensive account of the research evidence regarding how technology influences student engagement. One limitation of the literature, however, is the lack of detail regarding how teaching and learning practices were used to select and integrate technology into learning. For example, the methodology section of many studies does not include a pedagogical justification for why a particular technology was used or details about the design of the learning activity itself. Therefore, it often is unclear how teaching and learning practices may have affected student engagement levels. We revisit this issue in more detail at the end of this paper in our discussions of areas for future research and recommendations for practice. We initiated our literature review by conducting a broad search for articles published within the past 5 years, using the key words technology and higher education , in Google Scholar and the following research databases: Academic Search Complete, Communication & Mass Media Complete, Computers & Applied Sciences Complete, Education Research Complete, ERIC, PsycARTICLES, and PsycINFO . Our initial search revealed themes regarding which technologies were most prevalent in the literature (e.g., social networking, digital games), which then lead to several, more targeted searches of the same databases using specific keywords such as Facebook and student engagement. After both broad and targeted searches, we identified five technologies (web-conferencing software, blogs, wikis, social networking sites, and digital games) to include in our review.

We chose to focus on technologies for which there were multiple studies published, allowing us to identify areas of convergence and divergence in the literature and draw conclusions about positive and negative effects on student engagement. In total, we identified 69 articles relevant to our review, with 36 pertaining to social networking sites (21 for Facebook and 15 for Twitter ), 14 pertaining to digital games, seven pertaining to wikis, and six pertaining to blogs and web-conferencing software respectively. Articles were categorized according to their influence on specific types of student engagement, which will be described in more detail below. In some instances, one article pertained to multiple types of engagement. In the sections that follow, we will provide an overview of student engagement, including an explanation of common definitions and indicators of engagement, followed by a synthesis of how each type of technology influences student engagement. Finally, we will discuss areas for future research and make recommendations for practice.

• Student engagement

Interest in student engagement began over 70 years ago with Ralph Tyler’s research on the relationship between time spent on coursework and learning (Axelson & Flick, 2011 ; Kuh, 2009 ). Since then, the study of student engagement has evolved and expanded considerably, through the seminal works of Pace ( 1980 ; 1984 ) and Astin ( 1984 ) about how quantity and quality of student effort affect learning and many more recent studies on the environmental conditions and individual dispositions that contribute to student engagement (Bakker, Vergel, & Kuntze, 2015 ; Gilboy, Heinerichs, & Pazzaglia, 2015 ; Martin, Goldwasser, & Galentino, 2017 ; Pellas, 2014 ). Perhaps the most well-known resource on student engagement is the National Survey of Student Engagement (NSSE), an instrument designed to assess student participation in various educational activities (Kuh, 2009 ). The NSSE and other engagement instruments like it have been used in many studies that link student engagement to positive student outcomes such as higher grades, retention, persistence, and completion (Leach, 2016 ; McClenney, Marti, & Adkins, 2012 ; Trowler & Trowler, 2010 ), further convincing universities that student engagement is an important factor in the teaching and learning process. However, despite the increased interest in student engagement, its meaning is generally not well understood or agreed upon.

Student engagement is a broad and complex phenomenon for which there are many definitions grounded in psychological, social, and/or cultural perspectives (Fredricks et al., 1994; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ). Review of definitions revealed that student engagement is defined in two ways. One set of definitions refer to student engagement as a desired outcome reflective of a student’s thoughts, feelings, and behaviors about learning. For example, Kahu ( 2013 ) defines student engagement as an “individual psychological state” that includes a student’s affect, cognition, and behavior (p. 764). Other definitions focus primarily on student behavior, suggesting that engagement is the “extent to which students are engaging in activities that higher education research has shown to be linked with high-quality learning outcomes” (Krause & Coates, 2008 , p. 493) or the “quality of effort and involvement in productive learning activities” (Kuh, 2009 , p. 6). Another set of definitions refer to student engagement as a process involving both the student and the university. For example, Trowler ( 2010 ) defined student engagement as “the interaction between the time, effort and other relevant resources invested by both students and their institutions intended to optimize the student experience and enhance the learning outcomes and development of students and the performance, and reputation of the institution” (p. 2). Similarly, the NSSE website indicates that student engagement is “the amount of time and effort students put into their studies and other educationally purposeful activities” as well as “how the institution deploys its resources and organizes the curriculum and other learning opportunities to get students to participate in activities that decades of research studies show are linked to student learning” (Center for Postsecondary Research, 2017 , para. 1).

Many existing models of student engagement reflect the latter set of definitions, depicting engagement as a complex, psychosocial process involving both student and university characteristics. Such models organize the engagement process into three areas: factors that influence student engagement (e.g., institutional culture, curriculum, and teaching practices), indicators of student engagement (e.g., interest in learning, interaction with instructors and peers, and meaningful processing of information), and outcomes of student engagement (e.g., academic achievement, retention, and personal growth) (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ). In this review, we examine the literature to determine whether technology influences student engagement. In addition, we will use Fredricks et al. ( 2004 ) typology of student engagement to organize and present research findings, which suggests that there are three types of engagement (behavioral, emotional, and cognitive). The typology is useful because it is broad in scope, encompassing different types of engagement that capture a range of student experiences, rather than narrower typologies that offer specific or prescriptive conceptualizations of student engagement. In addition, this typology is student-centered, focusing exclusively on student-focused indicators rather than combining student indicators with confounding variables, such as faculty behavior, curriculum design, and campus environment (Coates, 2008 ; Kuh, 2009 ). While such variables are important in the discussion of student engagement, perhaps as factors that may influence engagement, they are not true indicators of student engagement. Using the typology as a guide, we examined recent student engagement research, models, and measures to gain a better understanding of how behavioral, emotional, and cognitive student engagement are conceptualized and to identify specific indicators that correspond with each type of engagement, as shown in Fig. 1 .

Conceptual framework of types and indicators of student engagement

Behavioral engagement is the degree to which students are actively involved in learning activities (Fredricks et al., 2004 ; Kahu, 2013 ; Zepke, 2014 ). Indicators of behavioral engagement include time and effort spent participating in learning activities (Coates, 2008 ; Fredricks et al., 2004 ; Kahu, 2013 ; Kuh, 2009 ; Lam et al., 2012 ; Lester, 2013 ; Trowler, 2010 ) and interaction with peers, faculty, and staff (Coates, 2008 ; Kahu, 2013 ; Kuh, 2009 ; Bryson & Hand, 2007 ; Wimpenny & Savin-Baden, 2013 : Zepke & Leach, 2010 ). Indicators of behavioral engagement reflect observable student actions and most closely align with Pace ( 1980 ) and Astin’s ( 1984 ) original conceptualizations of student engagement as quantity and quality of effort towards learning. Emotional engagement is students’ affective reactions to learning (Fredricks et al., 2004 ; Lester, 2013 ; Trowler, 2010 ). Indicators of emotional engagement include attitudes, interests, and values towards learning (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ; Witkowski & Cornell, 2015 ) and a perceived sense of belonging within a learning community (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ). Emotional engagement often is assessed using self-report measures (Fredricks et al., 2004 ) and provides insight into how students feel about a particular topic, delivery method, or instructor. Finally, cognitive engagement is the degree to which students invest in learning and expend mental effort to comprehend and master content (Fredricks et al., 2004 ; Lester, 2013 ). Indicators of cognitive engagement include: motivation to learn (Lester, 2013 ; Richardson & Newby, 2006 ; Zepke & Leach, 2010 ); persistence to overcome academic challenges and meet/exceed requirements (Fredricks et al., 2004 ; Kuh, 2009 ; Trowler, 2010 ); and deep processing of information (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ; Richardson & Newby, 2006 ) through critical thinking (Coates, 2008 ; Witkowski & Cornell, 2015 ), self-regulation (e.g., set goals, plan, organize study effort, and monitor learning; Fredricks et al., 2004 ; Lester, 2013 ), and the active construction of knowledge (Coates, 2008 ; Kuh, 2009 ). While cognitive engagement includes motivational aspects, much of the literature focuses on how students use active learning and higher-order thinking, in some form, to achieve content mastery. For example, there is significant emphasis on the importance of deep learning, which involves analyzing new learning in relation previous knowledge, compared to surface learning, which is limited to memorization, recall, and rehearsal (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ).

While each type of engagement has distinct features, there is some overlap across cognitive, behavioral, and emotional domains. In instances where an indicator could correspond with more than one type of engagement, we chose to match the indicator to the type of engagement that most closely aligned, based on our review of the engagement literature and our interpretation of the indicators. Similarly, there is also some overlap among indicators. As a result, we combined and subsumed similar indicators found in the literature, where appropriate, to avoid redundancy. Achieving an in-depth understanding of student engagement and associated indicators was an important pre-cursor to our review of the technology literature. Very few articles used the term student engagement as a dependent variable given the concept is so broad and multidimensional. We found that specific indicators (e.g., interaction, sense of belonging, and knowledge construction) of student engagement were more common in the literature as dependent variables. Next, we will provide a synthesis of the findings regarding how different types of technology influence behavioral, emotional, and cognitive student engagement and associated indicators.

Influence of technology on student engagement

We identified five technologies post-literature search (i.e., web-conferencing, blogs, wikis, social networking sites , and digital games) to include in our review, based on frequency in which they appeared in the literature over the past 5 years. One commonality among these technologies is their potential value in supporting a constructivist approach to learning, characterized by the active discovery of knowledge through reflection of experiences with one’s environment, the connection of new knowledge to prior knowledge, and interaction with others (Boghossian, 2006 ; Clements, 2015 ). Another commonality is that most of the technologies, except perhaps for digital games, are designed primarily to promote interaction and collaboration with others. Our search yielded very few studies on how informational technologies, such as video lectures and podcasts, influence student engagement. Therefore, these technologies are notably absent from our review. Unlike the technologies we identified earlier, informational technologies reflect a behaviorist approach to learning in which students are passive recipients of knowledge that is transmitted from an expert (Boghossian, 2006 ). The lack of recent research on how informational technologies affect student engagement may be due to the increasing shift from instructor-centered, behaviorist approaches to student-centered, constructivist approaches within higher education (Haggis, 2009 ; Wright, 2011 ) along with the ubiquity of web 2.0 technologies.

• Web-conferencing

Web-conferencing software provides a virtual meeting space where users login simultaneously and communicate about a given topic. While each software application is unique, many share similar features such as audio, video, or instant messaging options for real-time communication; screen sharing, whiteboards, and digital pens for presentations and demonstrations; polls and quizzes for gauging comprehension or eliciting feedback; and breakout rooms for small group work (Bower, 2011 ; Hudson, Knight, & Collins, 2012 ; Martin, Parker, & Deale, 2012 ; McBrien, Jones, & Cheng, 2009 ). Of the technologies included in this literature review, web-conferencing software most closely mimics the face-to-face classroom environment, providing a space where instructors and students can hear and see each other in real-time as typical classroom activities (i.e., delivering lectures, discussing course content, asking/answering questions) are carried out (Francescucci & Foster, 2013 ; Hudson et al., 2012 ). Studies on web-conferencing software deployed Adobe Connect, Cisco WebEx, Horizon Wimba, or Blackboard Collaborate and made use of multiple features, such as screen sharing, instant messaging, polling, and break out rooms. In addition, most of the studies integrated web-conferencing software into courses on a voluntary basis to supplement traditional instructional methods (Andrew, Maslin-Prothero, & Ewens, 2015 ; Armstrong & Thornton, 2012 ; Francescucci & Foster, 2013 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ). Existing studies on web-conferencing pertain to all three types of student engagement.

Studies on web-conferencing and behavioral engagement reveal mixed findings. For example, voluntary attendance in web-conferencing sessions ranged from 54 to 57% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ) and, in a comparison between a blended course with regular web-conferencing sessions and a traditional, face-to-face course, researchers found no significant difference in student attendance in courses. However, students in the blended course reported higher levels of class participation compared to students in the face-to-face course (Francescucci & Foster, 2013 ). These findings suggest while web-conferencing may not boost attendance, especially if voluntary, it may offer more opportunities for class participation, perhaps through the use of communication channels typically not available in a traditional, face-to-face course (e.g., instant messaging, anonymous polling). Studies on web-conferencing and interaction, another behavioral indicator, support this assertion. For example, researchers found that students use various features of web-conferencing software (e.g., polling, instant message, break-out rooms) to interact with peers and the instructor by asking questions, expressing opinions and ideas, sharing resources, and discussing academic content (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ).

Studies on web-conferencing and cognitive engagement are more conclusive than those for behavioral engagement, although are fewer in number. Findings suggest that students who participated in web-conferencing demonstrated critical reflection and enhanced learning through interactions with others (Armstrong & Thornton, 2012 ), higher-order thinking (e.g., problem-solving, synthesis, evaluation) in response to challenging assignments (Wdowik, 2014 ), and motivation to learn, particularly when using polling features (Hudson et al., 2012 ). There is only one study examining how web-conferencing affects emotional engagement, although it is positive suggesting that students who participated in web-conferences had higher levels of interest in course content than those who did not (Francescucci & Foster, 2013 ). One possible reason for the positive cognitive and emotional engagement findings may be that web-conferencing software provides many features that promote active learning. For example, whiteboards and breakout rooms provide opportunities for real-time, collaborative problem-solving activities and discussions. However, additional studies are needed to isolate and compare specific web-conferencing features to determine which have the greatest effect on student engagement.

A blog, which is short for Weblog, is a collection of personal journal entries, published online and presented chronologically, to which readers (or subscribers) may respond by providing additional commentary or feedback. In order to create a blog, one must compose content for an entry, which may include text, hyperlinks, graphics, audio, or video, publish the content online using a blogging application, and alert subscribers that new content is posted. Blogs may be informal and personal in nature or may serve as formal commentary in a specific genre, such as in politics or education (Coghlan et al., 2007 ). Fortunately, many blog applications are free, and many learning management systems (LMSs) offer a blogging feature that is seamlessly integrated into the online classroom. The ease of blogging has attracted attention from educators, who currently use blogs as an instructional tool for the expression of ideas, opinions, and experiences and for promoting dialogue on a wide range of academic topics (Garrity, Jones, VanderZwan, de la Rocha, & Epstein, 2014 ; Wang, 2008 ).

Studies on blogs show consistently positive findings for many of the behavioral and emotional engagement indicators. For example, students reported that blogs promoted interaction with others, through greater communication and information sharing with peers (Chu, Chan, & Tiwari, 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), and analyses of blog posts show evidence of students elaborating on one another’s ideas and sharing experiences and conceptions of course content (Sharma & Tietjen, 2016 ). Blogs also contribute to emotional engagement by providing students with opportunities to express their feelings about learning and by encouraging positive attitudes about learning (Dos & Demir, 2013 ; Chu et al., 2012 ; Yang & Chang, 2012 ). For example, Dos and Demir ( 2013 ) found that students expressed prejudices and fears about specific course topics in their blog posts. In addition, Yang and Chang ( 2012 ) found that interactive blogging, where comment features were enabled, lead to more positive attitudes about course content and peers compared to solitary blogging, where comment features were disabled.

The literature on blogs and cognitive engagement is less consistent. Some studies suggest that blogs may help students engage in active learning, problem-solving, and reflection (Chawinga, 2017 ; Chu et al., 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), while other studies suggest that students’ blog posts show very little evidence of higher-order thinking (Dos & Demir, 2013 ; Sharma & Tietjen, 2016 ). The inconsistency in findings may be due to the wording of blog instructions. Students may not necessarily demonstrate or engage in deep processing of information unless explicitly instructed to do so. Unfortunately, it is difficult to determine whether the wording of blog assignments contributed to the mixed results because many of the studies did not provide assignment details. However, studies pertaining to other technologies suggest that assignment wording that lacks specificity or requires low-level thinking can have detrimental effects on student engagement outcomes (Hou, Wang, Lin, & Chang, 2015 ; Prestridge, 2014 ). Therefore, blog assignments that are vague or require only low-level thinking may have adverse effects on cognitive engagement.

A wiki is a web page that can be edited by multiple users at once (Nakamaru, 2012 ). Wikis have gained popularity in educational settings as a viable tool for group projects where group members can work collaboratively to develop content (i.e., writings, hyperlinks, images, graphics, media) and keep track of revisions through an extensive versioning system (Roussinos & Jimoyiannis, 2013 ). Most studies on wikis pertain to behavioral engagement, with far fewer studies on cognitive engagement and none on emotional engagement. Studies pertaining to behavioral engagement reveal mixed results, with some showing very little enduring participation in wikis beyond the first few weeks of the course (Nakamaru, 2012 ; Salaber, 2014 ) and another showing active participation, as seen in high numbers of posts and edits (Roussinos & Jimoyiannis, 2013 ). The most notable difference between these studies is the presence of grading, which may account for the inconsistencies in findings. For example, in studies where participation was low, wikis were ungraded, suggesting that students may need extra motivation and encouragement to use wikis (Nakamaru, 2012 ; Salaber, 2014 ). Findings regarding the use of wikis for promoting interaction are also inconsistent. In some studies, students reported that wikis were useful for interaction, teamwork, collaboration, and group networking (Camacho, Carrión, Chayah, & Campos, 2016 ; Martínez, Medina, Albalat, & Rubió, 2013 ; Morely, 2012 ; Calabretto & Rao, 2011 ) and researchers found evidence of substantial collaboration among students (e.g., sharing ideas, opinions, and points of view) in wiki activity (Hewege & Perera, 2013 ); however, Miller, Norris, and Bookstaver ( 2012 ) found that only 58% of students reported that wikis promoted collegiality among peers. The findings in the latter study were unexpected and may be due to design flaws in the wiki assignments. For example, the authors noted that wiki assignments were not explicitly referred to in face-to-face classes; therefore, this disconnect may have prevented students from building on interactive momentum achieved during out-of-class wiki assignments (Miller et al., 2012 ).

Studies regarding cognitive engagement are limited in number but more consistent than those concerning behavioral engagement, suggesting that wikis promote high levels of knowledge construction (i.e., evaluation of arguments, the integration of multiple viewpoints, new understanding of course topics; Hewege & Perera, 2013 ), and are useful for reflection, reinforcing course content, and applying academic skills (Miller et al., 2012 ). Overall, there is mixed support for the use of wikis to promote behavioral engagement, although making wiki assignments mandatory and explicitly referring to wikis in class may help bolster participation and interaction. In addition, there is some support for using wikis to promote cognitive engagement, but additional studies are needed to confirm and expand on findings as well as explore the effect of wikis on emotional engagement.

Social networking sites

Social networking is “the practice of expanding knowledge by making connections with individuals of similar interests” (Gunawardena et al., 2009 , p. 4). Social networking sites, such as Facebook, Twitter, Instagram, and LinkedIn, allow users to create and share digital content publicly or with others to whom they are connected and communicate privately through messaging features. Two of the most popular social networking sites in the educational literature are Facebook and Twitter (Camus, Hurt, Larson, & Prevost, 2016 ; Manca & Ranieri, 2013 ), which is consistent with recent statistics suggesting that both sites also are exceedingly popular among the general population (Greenwood, Perrin, & Duggan, 2016 ). In the sections that follow, we examine how both Facebook and Twitter influence different types of student engagement.

Facebook is a web-based service that allows users to create a public or private profile and invite others to connect. Users may build social, academic, and professional connections by posting messages in various media formats (i.e., text, pictures, videos) and commenting on, liking, and reacting to others’ messages (Bowman & Akcaoglu, 2014 ; Maben, Edwards, & Malone, 2014 ; Hou et al., 2015 ). Within an educational context, Facebook has often been used as a supplementary instructional tool to lectures or LMSs to support class discussions or develop, deliver, and share academic content and resources. Many instructors have opted to create private Facebook groups, offering an added layer of security and privacy because groups are not accessible to strangers (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Clements, 2015 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 ; Shraim, 2014 ; Maben et al., 2014 ; Manca & Ranieri, 2013 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ). The majority of studies on Facebook address behavioral indicators of student engagement, with far fewer focusing on emotional or cognitive engagement.

Studies that examine the influence of Facebook on behavioral engagement focus both on participation in learning activities and interaction with peers and instructors. In most studies, Facebook activities were voluntary and participation rates ranged from 16 to 95%, with an average of rate of 47% (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Fagioli, Rios-Aguilar, & Deil-Amen, 2015 ; Rambe, 2012 ; Staines & Lauchs, 2013 ). Participation was assessed by tracking how many students joined course- or university-specific Facebook groups (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Fagioli et al., 2015 ), visited or followed course-specific Facebook pages (DiVall & Kirwin, 2012 ; Staines & Lauchs, 2013 ), or posted at least once in a course-specific Facebook page (Rambe, 2012 ). The lowest levels of participation (16%) arose from a study where community college students were invited to use the Schools App, a free application that connects students to their university’s private Facebook community. While the authors acknowledged that building an online community of college students is difficult (Fagioli et al., 2015 ), downloading the Schools App may have been a deterrent to widespread participation. In addition, use of the app was not tied to any specific courses or assignments; therefore, students may have lacked adequate incentive to use it. The highest level of participation (95%) in the literature arose from a study in which the instructor created a Facebook page where students could find or post study tips or ask questions. Followership to the page was highest around exams, when students likely had stronger motivations to access study tips and ask the instructor questions (DiVall & Kirwin, 2012 ). The wide range of participation in Facebook activities suggests that some students may be intrinsically motivated to participate, while other students may need some external encouragement. For example, Bahati ( 2015 ) found that when students assumed that a course-specific Facebook was voluntary, only 23% participated, but when the instructor confirmed that the Facebook group was, in fact, mandatory, the level of participation rose to 94%.

While voluntary participation in Facebook activities may be lower than desired or expected (Dyson, Vickers, Turtle, Cowan, & Tassone, 2015 ; Fagioli et al., 2015 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ), students seem to have a clear preference for Facebook compared to other instructional tools (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ). For example, in one study where an instructor shared course-related information in a Facebook group, in the LMS, and through email, the level of participation in the Facebook group was ten times higher than in email or the LMS (Clements, 2015 ). In other studies, class discussions held in Facebook resulted in greater levels of participation and dialogue than class discussions held in LMS discussion forums (Camus et al., 2016 ; Hurt et al., 2012 ; Kent, 2013 ). Researchers found that preference for Facebook over the university’s LMS is due to perceptions that the LMS is outdated and unorganized and reports that Facebook is more familiar, convenient, and accessible given that many students already visit the social networking site multiple times per day (Clements, 2015 ; Dougherty & Andercheck, 2014 ; Hurt et al., 2012 ; Kent, 2013 ). In addition, students report that Facebook helps them stay engaged in learning through collaboration and interaction with both peers and instructors (Bahati, 2015 ; Shraim, 2014 ), which is evident in Facebook posts where students collaborated to study for exams, consulted on technical and theoretical problem solving, discussed course content, exchanged learning resources, and expressed opinions as well as academic successes and challenges (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 Ivala & Gachago, 2012 ; Maben et al., 2014 ; Rambe, 2012 ; van Beynen & Swenson, 2016 ).

There is far less evidence in the literature about the use of Facebook for emotional and cognitive engagement. In terms of emotional engagement, studies suggest that students feel positively about being part of a course-specific Facebook group and that Facebook is useful for expressing feelings about learning and concerns for peers, through features such as the “like” button and emoticons (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Naghdipour & Eldridge, 2016 ). In addition, being involved in a course-specific Facebook group was positively related to students’ sense of belonging in the course (Dougherty & Andercheck, 2014 ). The research on cognitive engagement is less conclusive, with some studies suggesting that Facebook participation is related to academic persistence (Fagioli et al., 2015 ) and self-regulation (Dougherty & Andercheck, 2014 ) while other studies show low levels of knowledge construction in Facebook posts (Hou et al., 2015 ), particularly when compared to discussions held in the LMS. One possible reason may be because the LMS is associated with formal, academic interactions while Facebook is associated with informal, social interactions (Camus et al., 2016 ). While additional research is needed to confirm the efficacy of Facebook for promoting cognitive engagement, studies suggest that Facebook may be a viable tool for increasing specific behavioral and emotional engagement indicators, such as interactions with others and a sense of belonging within a learning community.

Twitter is a web-based service where subscribers can post short messages, called tweets, in real-time that are no longer than 140 characters in length. Tweets may contain hyperlinks to other websites, images, graphics, and/or videos and may be tagged by topic using the hashtag symbol before the designated label (e.g., #elearning). Twitter subscribers may “follow” other users and gain access to their tweets and also may “retweet” messages that have already been posted (Hennessy, Kirkpatrick, Smith, & Border, 2016 ; Osgerby & Rush, 2015 ; Prestridge, 2014 ; West, Moore, & Barry, 2015 ; Tiernan, 2014 ;). Instructors may use Twitter to post updates about the course, clarify expectations, direct students to additional learning materials, and encourage students to discuss course content (Bista, 2015 ; Williams & Whiting, 2016 ). Several of the studies on the use of Twitter included broad, all-encompassing measures of student engagement and produced mixed findings. For example, some studies suggest that Twitter increases student engagement (Evans, 2014 ; Gagnon, 2015 ; Junco, Heibergert, & Loken, 2011 ) while other studies suggest that Twitter has little to no influence on student engagement (Junco, Elavsky, & Heiberger, 2013 ; McKay, Sanko, Shekhter, & Birnbach, 2014 ). In both studies suggesting little to no influence on student engagement, Twitter use was voluntary and in one of the studies faculty involvement in Twitter was low, which may account for the negative findings (Junco et al., 2013 ; McKay et al., 2014 ). Conversely, in the studies that show positive findings, Twitter use was mandatory and often directly integrated with required assignments (Evans, 2014 ; Gagnon, 2015 ; Junco et al., 2011 ). Therefore, making Twitter use mandatory, increasing faculty involvement in Twitter, and integrating Twitter into assignments may help to increase student engagement.

Studies pertaining to specific behavioral student engagement indicators also reveal mixed findings. For example, in studies where course-related Twitter use was voluntary, 45-91% of students reported using Twitter during the term (Hennessy et al., 2016 ; Junco et al., 2013 ; Ross, Banow, & Yu, 2015 ; Tiernan, 2014 ; Williams & Whiting, 2016 ), but only 30-36% reported making contributions to the course-specific Twitter page (Hennessy et al., 2016 ; Tiernan, 2014 ; Ross et al., 2015 ; Williams & Whiting, 2016 ). The study that reported a 91% participation rate was unique because the course-specific Twitter page was accessible via a public link. Therefore, students who chose only to view the content (58%), rather than contribute to the page, did not have to create a Twitter account (Hennessy et al., 2016 ). The convenience of not having to create an account may be one reason for much higher participation rates. In terms of low participation rates, a lack of literacy, familiarity, and interest in Twitter , as well as a preference for Facebook , are cited as contributing factors (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). However, when the use of Twitter was required and integrated into class discussions, the participation rate was 100% (Gagnon, 2015 ). Similarly, 46% of students in one study indicated that they would have been more motivated to participate in Twitter activities if they were graded (Osgerby & Rush, 2015 ), again confirming the power of extrinsic motivating factors.

Studies also show mixed results for the use of Twitter to promote interactions with peers and instructors. Researchers found that when instructors used Twitter to post updates about the course, ask and answer questions, and encourage students to tweet about course content, there was evidence of student-student and student-instructor interactions in tweets (Hennessy et al., 2016 ; Tiernan, 2014 ). Some students echoed these findings, suggesting that Twitter is useful for sharing ideas and resources, discussing course content, asking the instructor questions, and networking (Chawinga, 2017 ; Evans, 2014 ; Gagnon, 2015 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 ; West et al., 2015 ) and is preferable over speaking aloud in class because it is more comfortable, less threatening, and more concise due to the 140 character limit (Gagnon, 2015 ; Mysko & Delgaty, 2015 ; Tiernan, 2014 ). Conversely, other students reported that Twitter was not useful for improving interaction because they viewed it predominately for social, rather than academic, interactions and they found the 140 character limit to be frustrating and restrictive. A theme among the latter studies was that a large proportion of the sample had never used Twitter before (Bista, 2015 ; McKay et al., 2014 ; Osgerby & Rush, 2015 ), which may have contributed to negative perceptions.

The literature on the use of Twitter for cognitive and emotional engagement is minimal but nonetheless promising in terms of promoting knowledge gains, the practical application of content, and a sense of belonging among users. For example, using Twitter to respond to questions that arose in lectures and tweet about course content throughout the term is associated with increased understanding of course content and application of knowledge (Kim et al., 2015 ; Tiernan, 2014 ; West et al., 2015 ). While the underlying mechanisms pertaining to why Twitter promotes an understanding of content and application of knowledge are not entirely clear, Tiernan ( 2014 ) suggests that one possible reason may be that Twitter helps to break down communication barriers, encouraging shy or timid students to participate in discussions that ultimately are richer in dialogue and debate. In terms of emotional engagement, students who participated in a large, class-specific Twitter page were more likely to feel a sense of community and belonging compared to those who did not participate because they could more easily find support from and share resources with other Twitter users (Ross et al., 2015 ). Despite the positive findings about the use of Twitter for cognitive and emotional engagement, more studies are needed to confirm existing results regarding behavioral engagement and target additional engagement indicators such as motivation, persistence, and attitudes, interests, and values about learning. In addition, given the strong negative perceptions of Twitter that still exist, additional studies are needed to confirm Twitter ’s efficacy for promoting different types of behavioral engagement among both novice and experienced Twitter users, particularly when compared to more familiar tools such as Facebook or LMS discussion forums.

• Digital games

Digital games are “applications using the characteristics of video and computer games to create engaging and immersive learning experiences for delivery of specified learning goals, outcomes and experiences” (de Freitas, 2006 , p. 9). Digital games often serve the dual purpose of promoting the achievement of learning outcomes while making learning fun by providing simulations of real-world scenarios as well as role play, problem-solving, and drill and repeat activities (Boyle et al., 2016 ; Connolly, Boyle, MacArthur, Hainey, & Boyle, 2012 ; Scarlet & Ampolos, 2013 ; Whitton, 2011 ). In addition, gamified elements, such as digital badges and leaderboards, may be integrated into instruction to provide additional motivation for completing assigned readings and other learning activities (Armier, Shepherd, & Skrabut, 2016 ; Hew, Huang, Chu, & Chiu, 2016 ). The pedagogical benefits of digital games are somewhat distinct from the other technologies addressed in this review, which are designed primarily for social interaction. While digital games may be played in teams or allow one player to compete against another, the focus of their design often is on providing opportunities for students to interact with academic content in a virtual environment through decision-making, problem-solving, and reward mechanisms. For example, a digital game may require students to adopt a role as CEO in a computer-simulated business environment, make decisions about a series of organizational issues, and respond to the consequences of those decisions. In this example and others, digital games use adaptive learning principles, where the learning environment is re-configured or modified in response to the actions and needs of students (Bower, 2016 ). Most of the studies on digital games focused on cognitive and emotional indicators of student engagement, in contrast to the previous technologies addressed in this review which primarily focused on behavioral indicators of engagement.

Existing studies provide support for the influence of digital games on cognitive engagement, through achieving a greater understanding of course content and demonstrating higher-order thinking skills (Beckem & Watkins, 2012 ; Farley, 2013 ; Ke, Xie, & Xie, 2016 ; Marriott, Tan, & Marriott, 2015 ), particularly when compared to traditional instructional methods, such as giving lectures or assigning textbook readings (Lu, Hallinger, & Showanasai, 2014 ; Siddique, Ling, Roberson, Xu, & Geng, 2013 ; Zimmermann, 2013 ). For example, in a study comparing courses that offered computer simulations of business challenges (e.g, implementing a new information technology system, managing a startup company, and managing a brand of medicine in a simulated market environment) and courses that did not, students in simulation-based courses reported higher levels of action-directed learning (i.e., connecting theory to practice in a business context) than students in traditional, non-simulation-based courses (Lu et al., 2014 ). Similarly, engineering students who participated in a car simulator game, which was designed to help students apply and reinforce the knowledge gained from lectures, demonstrated higher levels of critical thinking (i.e., analysis, evaluation) on a quiz than students who only attended lectures (Siddique et al., 2013 ).

Motivation is another cognitive engagement indicator that is linked to digital games (Armier et al., 2016 ; Chang & Wei, 2016 ; Dichev & Dicheva, 2017 ; Grimley, Green, Nilsen, & Thompson, 2012 ; Hew et al., 2016 ; Ibáñez, Di-Serio, & Delgado-Kloos, 2014 ; Ke et al., 2016 ; Liu, Cheng, & Huang, 2011 ; Nadolny & Halabi, 2016 ). Researchers found that incorporating gamified elements into courses, such as giving students digital rewards (e.g., redeemable points, trophies, and badges) for participating in learning activities or creating competition through the use of leaderboards where students can see how they rank against other students positively affects student motivation to complete learning tasks (Armier et al., 2016 ; Chang & Wei, 2016 ; Hew et al., 2016 ; Nadolny & Halabi, 2016 ). In addition, students who participated in gamified elements, such as trying to earn digital badges, were more motivated to complete particularly difficult learning activities (Hew et al., 2016 ) and showed persistence in exceeding learning requirements (Ibáñez et al., 2014 ). Research on emotional engagement may help to explain these findings. Studies suggest that digital games positively affect student attitudes about learning, evident in student reports that games are fun, interesting, and enjoyable (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), which may account for higher levels of student motivation in courses that offered digital games.

Research on digital games and behavioral engagement is more limited, with only one study suggesting that games lead to greater participation in educational activities (Hew et al., 2016 ). Therefore, more research is needed to explore how digital games may influence behavioral engagement. In addition, research is needed to determine whether the underlying technology associated with digital games (e.g., computer-based simulations and virtual realities) produce positive engagement outcomes or whether common mechanisms associated with both digital and non-digital games (e.g., role play, rewards, and competition) account for those outcomes. For example, studies in which non-digital, face-to-face games were used also showed positive effects on student engagement (Antunes, Pacheco, & Giovanela, 2012 ; Auman, 2011 ; Coffey, Miller, & Feuerstein, 2011 ; Crocco, Offenholley, & Hernandez, 2016 ; Poole, Kemp, Williams, & Patterson, 2014 ; Scarlet & Ampolos, 2013 ); therefore, it is unclear if and how digitizing games contributes to student engagement.

Discussion and implications

Student engagement is linked to a number of academic outcomes, such as retention, grade point average, and graduation rates (Carini et al., 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). As a result, universities have shown a strong interest in how to increase student engagement, particularly given rising external pressures to improve learning outcomes and prepare students for academic success (Axelson & Flick, 2011 ; Kuh, 2009 ). There are various models of student engagement that identify factors that influence student engagement (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ); however, none include the overt role of technology despite the growing trend and student demands to integrate technology into the learning experience (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ). Therefore, the primary purpose of our literature review was to explore whether technology influences student engagement. The secondary purpose was to address skepticism and uncertainty about pedagogical benefits of technology (Ashrafzadeh & Sayadian, 2015 ; Kopcha et al., 2016 ; Reid, 2014 ) by reviewing the literature regarding the efficacy of specific technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) for promoting student engagement and offering recommendations for effective implementation, which are included at the end of this paper. In the sections that follow, we provide an overview of the findings, an explanation of existing methodological limitations and areas for future research, and a list of best practices for integrating the technologies we reviewed into the teaching and learning process.

Summary of findings

Findings from our literature review provide preliminary support for including technology as a factor that influences student engagement in existing models (Table 1 ). One overarching theme is that most of the technologies we reviewed had a positive influence on multiple indicators of student engagement, which may lead to a larger return on investment in terms of learning outcomes. For example, digital games influence all three types of student engagement and six of the seven indicators we identified, surpassing the other technologies in this review. There were several key differences in the design and pedagogical use between digital games and other technologies that may explain these findings. First, digital games were designed to provide authentic learning contexts in which students could practice skills and apply learning (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Ke et al., 2016 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ), which is consistent with experiential learning and adult learning theories. Experiential learning theory suggests that learning occurs through interaction with one’s environment (Kolb, 2014 ) while adult learning theory suggests that adult learners want to be actively involved in the learning process and be able apply learning to real life situations and problems (Cercone, 2008 ). Second, students reported that digital games (and gamified elements) are fun, enjoyable, and interesting (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), feelings that are associated with a flow-like state where one is completely immersed in and engaged with the activity (Csikszentmihalyi, 1988 ; Weibel, Wissmath, Habegger, Steiner, & Groner, 2008 ). Third, digital games were closely integrated into the curriculum as required activities (Farley, 2013 ; Grimley et al., 2012 , Ke et al., 2016 ; Liu et al., 2011 ; Marriott et al., 2015 ; Siddique et al., 2013 ) as opposed to wikis, Facebook , and Twitter , which were often voluntary and used to supplement lectures (Dougherty & Andercheck, 2014 Nakamaru, 2012 ; Prestridge, 2014 ; Rambe, 2012 ).

Web-conferencing software and Facebook also yielded the most positive findings, influencing four of the seven indicators of student engagement, compared to other collaborative technologies, such as blogs, wikis, and Twitter . Web-conferencing software was unique due to the sheer number of collaborative features it offers, providing multiple ways for students to actively engage with course content (screen sharing, whiteboards, digital pens) and interact with peers and the instructor (audio, video, text chats, breakout rooms) (Bower, 2011 ; Hudson et al., 2012 ; Martin et al., 2012 ; McBrien et al., 2009 ); this may account for the effects on multiple indicators of student engagement. Positive findings regarding Facebook ’s influence on student engagement could be explained by a strong familiarity and preference for the social networking site (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ; Manca & Ranieri, 2013 ), compared to Twitter which was less familiar or interesting to students (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). Wikis had the lowest influence on student engagement, with mixed findings regarding behavioral engagement, limited, but conclusive findings, regarding one indicator of cognitive engagement (deep processing of information), and no studies pertaining to other indicators of cognitive engagement (motivation, persistence) or emotional engagement.

Another theme that arose was the prevalence of mixed findings across multiple technologies regarding behavioral engagement. Overall, the vast majority of studies addressed behavioral engagement, and we expected that technologies designed specifically for social interaction, such as web-conferencing, wikis, and social networking sites, would yield more conclusive findings. However, one possible reason for the mixed findings may be that the technologies were voluntary in many studies, resulting in lower than desired participation rates and missed opportunities for interaction (Armstrong & Thornton, 2012 ; Fagioli et al., 2015 ; Nakamaru, 2012 ; Rambe, 2012 ; Ross et al., 2015 ; Williams & Whiting, 2016 ), and mandatory in a few studies, yielding higher levels of participation and interaction (Bahati, 2015 ; Gagnon, 2015 ; Roussinos & Jimoyiannis, 2013 ). Another possible reason for the mixed findings is that measures of variables differed across studies. For example, in some studies participation meant that a student signed up for a Twitter account (Tiernan, 2014 ), used the Twitter account for class (Williams & Whiting, 2016 ), or viewed the course-specific Twitter page (Hennessy et al., 2016 ). The pedagogical uses of the technologies also varied considerably across studies, making it difficult to make comparisons. For example, Facebook was used in studies to share learning materials (Clements, 2015 ; Dyson et al., 2015 ), answer student questions about academic content or administrative issues (Rambe, 2012 ), prepare for upcoming exams and share study tips (Bowman & Akcaoglu, 2014 ; DiVall & Kirwin, 2012 ), complete group work (Hou et al., 2015 ; Staines & Lauchs, 2013 ), and discuss course content (Camus et al., 2016 ; Kent, 2013 ; Hurt et al., 2012 ). Finally, cognitive indicators (motivation and persistence) drew the fewest amount of studies, which suggests that research is needed to determine whether technologies affect these indicators.

Methodological limitations

While there appears to be preliminary support for the use of many of the technologies to promote student engagement, there are significant methodological limitations in the literature and, as a result, findings should be interpreted with caution. First, many studies used small sample sizes and were limited to one course, one degree level, and one university. Therefore, generalizability is limited. Second, very few studies used experimental or quasi-experimental designs; therefore, very little evidence exists to substantiate a cause and effect relationship between technologies and student engagement indicators. In addition, in many studies that did use experimental or quasi-experimental designs, participants were not randomized; rather, participants who volunteered to use a specific technology were compared to those who chose not to use the technology. As a result, there is a possibility that fundamental differences between users and non-users could have affected the engagement results. Furthermore, many of the studies did not isolate specific technological features (e.g, using only the breakout rooms for group work in web-conferencing software, rather than using the chat feature, screen sharing, and breakout rooms for group work). Using multiple features at once could have conflated student engagement results. Third, many studies relied on one source to measure technological and engagement variables (single source bias), such as self-report data (i.e., reported usage of technology and perceptions of student engagement), which may have affected the validity of the results. Fourth, many studies were conducted during a very brief timeframe, such as one academic term. As a result, positive student engagement findings may be attributed to a “novelty effect” (Dichev & Dicheva, 2017 ) associated with using a new technology. Finally, many studies lack adequate details about learning activities, raising questions about whether poor instructional design may have adversely affected results. For example, an instructor may intend to elicit higher-order thinking from students, but if learning activity instructions are written using low-level verbs, such as identify, describe, and summarize, students will be less likely to engage in higher-order thinking.

Areas for future research

The findings of our literature review suggest that the influence of technology on student engagement is still a developing area of knowledge that requires additional research to build on promising, but limited, evidence, clarify mixed findings, and address several gaps in the literature. As such, our recommendations for future areas of research are as follows:

Examine the effect of collaborative technologies (i.e., web-conferencing, blogs, wikis, social networking sites ) on emotional and cognitive student engagement. There are significant gaps in the literature regarding whether these technologies affect attitudes, interests, and values about learning; a sense of belonging within a learning community; motivation to learn; and persistence to overcome academic challenges and meet or exceed requirements.

Clarify mixed findings, particularly regarding how web-conferencing software, wikis, and Facebook and Twitter affect participation in learning activities. Researchers should make considerable efforts to gain consensus or increase consistency on how participation is measured (e.g., visited Facebook group or contributed one post a week) in order to make meaningful comparisons and draw conclusions about the efficacy of various technologies for promoting behavioral engagement. In addition, further research is needed to clarify findings regarding how wikis and Twitter influence interaction and how blogs and Facebook influence deep processing of information. Future research studies should include justifications for the pedagogical use of specific technologies and detailed instructions for learning activities to minimize adverse findings from poor instructional design and to encourage replication.

Conduct longitudinal studies over several academic terms and across multiple academic disciplines, degree levels, and institutions to determine long-term effects of specific technologies on student engagement and to increase generalizability of findings. Also, future studies should take individual factors into account, such as gender, age, and prior experience with the technology. Studies suggest that a lack of prior experience or familiarity with Twitter was a barrier to Twitter use in educational settings (Bista, 2015 , Mysko & Delgaty, 2015 , Tiernan, 2014 ); therefore, future studies should take prior experience into account.

Compare student engagement outcomes between and among different technologies and non-technologies. For example, studies suggest that students prefer Facebook over Twitter (Bista, 2015 ; Osgerby & Rush, 2015 ), but there were no studies that compared these technologies for promoting student engagement. Also, studies are needed to isolate and compare different features within the same technology to determine which might be most effective for increasing engagement. Finally, studies on digital games (Beckem & Watkins, 2012 ; Grimley et al., 2012 ; Ke et al., 2016 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ) and face-to-face games (Antunes et al., 2012 ; Auman, 2011 ; Coffey et al., 2011 ; Crocco et al., 2016 ; Poole et al., 2014 ; Scarlet & Ampolos, 2013 ) show similar, positive effects on student engagement, therefore, additional research is needed to determine the degree to which the delivery method (i.e.., digital versus face-to-face) accounts for positive gains in student engagement.

Determine whether other technologies not included in this review influence student engagement. Facebook and Twitter regularly appear in the literature regarding social networking, but it is unclear how other popular social networking sites, such as LinkedIn, Instagram, and Flickr, influence student engagement. Future research should focus on the efficacy of these and other popular social networking sites for promoting student engagement. In addition, there were very few studies about whether informational technologies, which involve the one-way transmission of information to students, affect different types of student engagement. Future research should examine whether informational technologies, such as video lectures, podcasts, and pre-recorded narrated Power Point presentations or screen casts, affect student engagement. Finally, studies should examine the influence of mobile software and technologies, such as educational apps or smartphones, on student engagement.

Achieve greater consensus on the meaning of student engagement and its distinction from similar concepts in the literature, such as social and cognitive presence (Garrison & Arbaugh, 2007 )

Recommendations for practice

Despite the existing gaps and mixed findings in the literature, we were able to compile a list of recommendations for when and how to use technology to increase the likelihood of promoting student engagement. What follows is not an exhaustive list; rather, it is a synthesis of both research findings and lessons learned from the studies we reviewed. There may be other recommendations to add to this list; however, our intent is to provide some useful information to help address barriers to technology integration among faculty who feel uncertain or unprepared to use technology (Ashrafzadeh & Sayadian, 2015 ; Hauptman, 2015 ; Kidd et al., 2016 ; Reid, 2014 ) and to add to the body of practical knowledge in instructional design and delivery. Our recommendations for practice are as follows:

Consider context before selecting technologies. Contextual factors such as existing technological infrastructure and requirements, program and course characteristics, and the intended audience will help determine which technologies, if any, are most appropriate (Bullen & Morgan, 2011 ; Bullen, Morgan, & Qayyum, 2011 ). For example, requiring students to use a blog that is not well integrated with the existing LMS may prove too frustrating for both the instructor and students. Similarly, integrating Facebook- and Twitter- based learning activities throughout a marketing program may be more appropriate, given the subject matter, compared to doing so in an engineering or accounting program where social media is less integral to the profession. Finally, do not assume that students appreciate or are familiar with all technologies. For example, students who did not already have Facebook or Twitter accounts were less likely to use either for learning purposes and perceived setting up an account to be an increase in workload (Bista, 2015 , Clements, 2015 ; DiVall & Kirwin, 2012 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 , Tiernan, 2014 ). Therefore, prior to using any technology, instructors may want to determine how many students already have accounts and/or are familiar with the technology.

Carefully select technologies based on their strengths and limitations and the intended learning outcome. For example, Twitter is limited to 140 characters, making it a viable tool for learning activities that require brevity. In one study, an instructor used Twitter for short pop quizzes during lectures, where the first few students to tweet the correct answer received additional points (Kim et al., 2015 ), which helped students practice applying knowledge. In addition, studies show that students perceive Twitter and Facebook to be primarily for social interactions (Camus et al., 2016 ; Ross et al., 2015 ), which may make these technologies viable tools for sharing resources, giving brief opinions about news stories pertaining to course content, or having casual conversations with classmates rather than full-fledged scholarly discourse.

Incentivize students to use technology, either by assigning regular grades or giving extra credit. The average participation rates in voluntary web-conferencing, Facebook , and Twitter learning activities in studies we reviewed was 52% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Divall & Kirwin, 2012 ; Dougherty & Andercheck, 2014 ; Fagioli et al., 2015 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Rambe, 2012 ; Ross et al., 2015 ; Staines & Lauchs, 2013 ; Tiernan, 2014 ; Williams & Whiting, 2016 ). While there were far fewer studies on the use of technology for graded or mandatory learning activities, the average participation rate reported in those studies was 97% (Bahati2015; Gagnon, 2015 ), suggesting that grading may be a key factor in ensuring students participate.

Communicate clear guidelines for technology use. Prior to the implementation of technology in a course, students may benefit from an overview the technology, including its navigational features, privacy settings, and security (Andrew et al., 2015 ; Hurt et al., 2012 ; Martin et al., 2012 ) and a set of guidelines for how to use the technology effectively and professionally within an educational setting (Miller et al., 2012 ; Prestridge, 2014 ; Staines & Lauchs, 2013 ; West et al., 2015 ). In addition, giving students examples of exemplary and poor entries and posts may also help to clarify how they are expected to use the technology (Shraim, 2014 ; Roussinos & Jimoyiannis, 2013 ). Also, if instructors expect students to use technology to demonstrate higher-order thinking or to interact with peers, there should be explicit instructions to do so. For example, Prestridge ( 2014 ) found that students used Twitter to ask the instructor questions but very few interacted with peers because they were not explicitly asked to do so. Similarly, Hou et al., 2015 reported low levels of knowledge construction in Facebook , admitting that the wording of the learning activity (e.g., explore and present applications of computer networking) and the lack of probing questions in the instructions may have been to blame.

Use technology to provide authentic and integrated learning experiences. In many studies, instructors used digital games to simulate authentic environments in which students could apply new knowledge and skills, which ultimately lead to a greater understanding of content and evidence of higher-order thinking (Beckem & Watkins, 2012 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ). For example, in one study, students were required to play the role of a stock trader in a simulated trading environment and they reported that the simulation helped them engage in critical reflection, enabling them to identify their mistakes and weaknesses in their trading approaches and strategies (Marriott et al., 2015 ). In addition, integrating technology into regularly-scheduled classroom activities, such as lectures, may help to promote student engagement. For example, in one study, the instructor posed a question in class, asked students to respond aloud or tweet their response, and projected the Twitter page so that everyone could see the tweets in class, which lead to favorable comments about the usefulness of Twitter to promote engagement (Tiernan, 2014 ).

Actively participate in using the technologies assigned to students during the first few weeks of the course to generate interest (Dougherty & Andercheck, 2014 ; West et al., 2015 ) and, preferably, throughout the course to answer questions, encourage dialogue, correct misconceptions, and address inappropriate behavior (Bowman & Akcaoglu, 2014 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Roussinos & Jimoyiannis, 2013 ). Miller et al. ( 2012 ) found that faculty encouragement and prompting was associated with increases in students’ expression of ideas and the degree to which they edited and elaborated on their peers’ work in a course-specific wiki.

Be mindful of privacy, security, and accessibility issues. In many studies, instructors took necessary steps to help ensure privacy and security by creating closed Facebook groups and private Twitter pages, accessible only to students in the course (Bahati, 2015 ; Bista, 2015 ; Bowman & Akcaoglu, 2014 ; Esteves, 2012 ; Rambe, 2012 ; Tiernan, 2014 ; Williams & Whiting, 2016 ) and by offering training to students on how to use privacy and security settings (Hurt et al., 2012 ). Instructors also made efforts to increase accessibility of web-conferencing software by including a phone number for students unable to access audio or video through their computer and by recording and archiving sessions for students unable to attend due to pre-existing conflicts (Andrew et al., 2015 ; Martin et al., 2012 ). In the future, instructors should also keep in mind that some technologies, like Facebook and Twitter , are not accessible to students living in China; therefore, alternative arrangements may need to be made.

In 1985, Steve Jobs predicted that computers and software would revolutionize the way we learn. Over 30 years later, his prediction has yet to be fully confirmed in the student engagement literature; however, our findings offer preliminary evidence that the potential is there. Of the technologies we reviewed, digital games, web-conferencing software, and Facebook had the most far-reaching effects across multiple types and indicators of student engagement, suggesting that technology should be considered a factor that influences student engagement in existing models. Findings regarding blogs, wikis, and Twitter, however, are less convincing, given a lack of studies in relation to engagement indicators or mixed findings. Significant methodological limitations may account for the wide range of findings in the literature. For example, small sample sizes, inconsistent measurement of variables, lack of comparison groups, and missing details about specific, pedagogical uses of technologies threaten the validity and reliability of findings. Therefore, more rigorous and robust research is needed to confirm and build upon limited but positive findings, clarify mixed findings, and address gaps particularly regarding how different technologies influence emotional and cognitive indicators of engagement.

Abbreviations

Learning management system

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This research was supported in part by a Laureate Education, Incl. David A. Wilson research grant study awarded to the second author, “A Comparative Analysis of Student Engagement and Critical Thinking in Two Approaches to the Online Classroom”.

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Schindler, L.A., Burkholder, G.J., Morad, O.A. et al. Computer-based technology and student engagement: a critical review of the literature. Int J Educ Technol High Educ 14 , 25 (2017). https://doi.org/10.1186/s41239-017-0063-0

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Technology research topics must identify a broad area of research on technologies, so an extremely technical topic can be overwhelming to write. Your technology research paper topic should be suitable for the academic level of your audience.

Tips for Choosing a Technology Research Topic

• Make sure it’s clear. Select a research topic with a clear main idea that you can explain in simple language. It should be able to capture the attention of the audience and keep them engaged in your research paper.
• Make sure it’s relevant. The technology research paper topic should be relevant to the understanding and academic level of the readers. It should enhance their knowledge of a specific technological topic, instead of simply providing vague, directionless ideas about different types of technologies.
• Employ approachable language. Even though you might be choosing a topic from complex technology research topics, the language should be simple. It can be field-specific, but the technical terms used must be basic and easy to understand for the readers.
• Discuss innovations. New technologies get introduced frequently, which adds to the variety of technology research paper topics. Your research topic shouldn’t be limited to old or common technologies. Along with the famous technologies, it should include evolving technologies and introduce them to the audience.
• Be creative . With the rapid growth of technological development, some technology research topics have become increasingly common. It can be challenging to be creative with a topic that has been exhausted through numerous research papers. Your research topic should provide unique information to the audience, which can attract them to your work.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is a subject or a problem being studied by a researcher. It is the foundation of any research paper that sets the tone of the research. It should be broad with a wide range of information available for conducting research.

On the other hand, a research question is closely related to the research topic and is addressed in the study. The answer is formed through data analysis and interpretation. It is more field-specific and directs the research paper toward a specific aspect of a broad subject.

How to Create Strong Technology Research Questions

Technology research questions should be concise, specific, and original while showing a connection to the technology research paper topic. It should be researchable and answerable through analysis of a problem or issue. Make sure it is easy to understand and write within the given word limit and timeframe of the research paper.

Technology is an emerging field with several areas of study, so a strong research question is based on a specific part of a large technical field. For example, many technologies are used in branches of healthcare such as genetics and DNA. Therefore, a research paper about genetics technology should feature a research question that is exclusive to genetics technology only.

Top 10 Technology Research Paper Topics

1. the future of computer-assisted education.

The world shifted to digital learning in the last few years. Students were using the Internet to take online classes, online exams, and courses. Some people prefer distance learning courses over face-to-face classes now, as they only require modern technologies like laptops, mobile phones, and the Internet to study, complete assignments, and even attend lectures.

The demand for digital learning has increased, and it will be an essential part of the education system in the coming years. As a result of the increasing demand, the global digital learning market is expecting a growth of about 110 percent by 2026 .

2. Children’s Use of Social Media

Nowadays, parents allow their children to use the Internet from a very young age. A recent poll by C.S. Mott Children’s Hospital reported that 32 percent of parents allow their children aged seven to nine to use social media sites. This can expose them to cyber bullying and age-inappropriate content, as well as increase their dependence on technology.

Kids need to engage in physical activities and explore the world around them. Using social media sites in childhood can be negative for their personalities and brain health. Analyzing the advantages and disadvantages of the use of technology among young children can create an interesting research paper.

3. The Risks of Digital Voting

Digital voting is an easy way of casting and counting votes. It can save the cost and time associated with traveling to the polling station and getting a postal vote. However, it has a different set of security challenges. A research paper can list the major election security risks caused by digital voting.

Voting in an online format can expose your personal information and decisions to a hacker. As no computer device or software is completely unhackable, the voting system can be taken down, or the hacking may even go undetected.

4. Technology’s Impact on Society in 20 Years

Technological development has accelerated in the last decade. Current technology trends in innovation are focusing on artificial intelligence development, machine learning, and the development and implementation of robots.

Climate change has affected both human life and animal life. Climate technology can be used to deal with global warming in the coming years, and digital learning can make education available for everyone. This technology research paper can discuss the positive and negative effects of technology in 20 years.

5. The Reliability of Self-Driving Cars

Self-driving cars are one of the most exciting trends in technology today. It is a major technology of the future and one of the controversial technology topics. It is considered safer than human driving, but there are some risks involved. For example, edge cases are still common to experience while driving.

Edge cases are occasional and unpredictable situations that may lead to accidents and injuries. It includes difficult weather conditions, objects or animals on the road, and blocked roads. Self-driving cars may struggle to respond to edge cases appropriately, requiring the driver to employ common sense to handle the situation.

6. The Impact of Technology on Infertility

Assisted reproductive technology (ART) helps infertile couples get pregnant. It employs infertility techniques such as In-Vitro Fertilization (IVF) and Gamete Intrafallopian Transfer (GIFT).

Infertility technologies are included in the controversial technology topics because embryonic stem cell research requires extracted human embryos. So, the research can be considered unethical. It is an excellent research topic from the reproductive technology field.

7. Evolution of War Technology

Military technologies have improved throughout history. Modern technologies, such as airplanes, missiles, nuclear reactors, and drones, are essential for war management. Countries experience major innovation in technologies during wars to fulfill their military-specific needs.

Military technologies have controversial ideas and debates linked to them, as some people believe that it plays a role in wars. A research paper on war technology can help evaluate the role of technology in warfare.

8. Using Technology to Create Eco-Friendly Food Packaging

Food technologies and agricultural technologies are trying to manage climate change through eco-friendly food packaging. The materials used are biodegradable, sustainable, and have inbuilt technology that kills microbes harmful to human life.

Research on eco-friendly food packaging can discuss the ineffectiveness of current packaging strategies. The new food technologies used for packaging can be costly, but they are better for preserving foods and the environment.

9. Disease Diagnostics and Therapeutics Through DNA Cloning

Genetic engineering deals with genes and uses them as diagnostics and therapeutics. DNA cloning creates copies of genes or parts of DNA to study different characteristics. The findings are used for diagnosing different types of cancers and even hematological diseases.

Genetic engineering is also used for therapeutic cloning, which clones an embryo for studying diseases and treatments. DNA technology, gene editing, gene therapy, and similar topics are hot topics in technology research papers.

10. Artificial Intelligence in Mental Health Care

Mental health is a widely discussed topic around the world, making it perfect for technology research topics. The mental health care industry has more recently been using artificial intelligence tools and mental health technology like chatbots and virtual assistants to connect with patients.

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Artificial intelligence has the potential to improve the diagnosis and treatment of mental illness. It can help a health care provider with monitoring patient progress and assigning the right therapist based on provided data and information.

Other Examples of Technology Research Topics & Questions

Technology research topics.

• The connection between productivity and the use of digital tools
• The importance of medical technologies in the next years
• The consequences of addiction to technology
• The negative impact of social media
• The rise and future of blockchain technology

Technology Research Questions

• Is using technology in college classrooms a good or bad idea?
• What are the advantages of cloud technologies for pharmaceutical companies?
• Can new technologies help in treating morbid obesity?
• How to identify true and false information on social media
• Why is machine learning the future?

Choosing the Right Technology Research Topic

Since technology is a diverse field, it can be challenging to choose an interesting technology research topic. It is crucial to select a good research topic for a successful research paper. Any research is centered around the research topic, so it’s important to pick one carefully.

From cell phones to self-driving cars, technological development has completely transformed the world. It offers a wide range of topics to research, resulting in numerous options to choose from. We have compiled technology research topics from a variety of fields. You should select a topic that interests you, as you will be spending weeks researching and writing about it.

Technology Research Topics FAQ

Technology is important in education because it allows people to access educational opportunities globally through mobile technologies and the Internet. Students can enroll in online college degrees , courses, and attend online coding bootcamps . Technology has also made writing research papers easier with the tremendous amount of material available online.

Yes, technology can take over jobs as robotics and automation continue to evolve. However, the management of these technologies will still require human employees with technical backgrounds, such as artificial intelligence specialists, data scientists , and cloud engineers.

Solar panels and wind turbines are two forms of technology that help with climate change, as they convert energy efficiently without emitting greenhouse gases. Electric bikes run on lithium batteries and only take a few hours to charge, which makes them environmentally friendly. Carbon dioxide captures are a way of removing CO 2 from the atmosphere and storing it deep underground.

Technology helps companies manage client and employee data, store and protect important information, and develop strategies to stay ahead of competitors. Marketing technologies, such as Search Engine Optimization (SEO), are great for attracting customers online.

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411 Technology Research Paper Topics & Ideas

18 January 2024

last updated

Technology research topics are deeply engaged with the exploration of data science and big data analytics, an increasingly critical area as human societies generate vast amounts of information daily. Various themes cover the study of the Internet of Things (IoT) and data exchange, improving efficiency and decision-making. The implications of nanotechnology, designing and utilizing materials at the molecular or atomic level, are another captivating research option. In addition, technology research is probing into the potential of effective communication, a concept that uses many networks that people use as a medium to interact with others. Scientists can also investigate the progress and effects of edge computing, a method of optimizing cloud computing systems by performing data processing within the network. Thus, technology research topics are ceaselessly evolving, driving people toward an increasingly interconnected, efficient, and innovative future.

Hot Technology Research Paper Topics

• Advancements in Quantum Computing: A Paradigm Shift
• Breakthroughs in Nanotechnology: Promises and Challenges
• Artificial Intelligence Ethics: Deciphering the Grey Areas
• Augmented Reality in Education: Revolution or Hype?
• The Blockchain Revolution: Possibilities Beyond Cryptocurrency
• Biometric Technology: Privacy Concerns in the Modern World
• Internet of Things (IoT) and Cybersecurity: A Global Perspective
• Integrating Renewable Energy with Smart Grids: Challenges and Solutions
• Rise of Autonomous Vehicles: Implications for Urban Planning
• Machine Learning Applications in Healthcare: Promises and Perils
• Neurotechnology and Human Rights: Navigating the Uncharted
• Virtual Reality in Mental Health: Opportunities and Obstacles
• Deep Learning Techniques in Weather Prediction: An Analytical Study
• Space Technology and Climate Change: A Symbiotic Relationship
• 5G Network Technology: Exploring Unforeseen Risks and Rewards
• Crisis Management in Social Media: Analyzing Algorithms and Bias
• Innovations in AgriTech: Shaping the Future of Sustainable Farming
• 3D Printing Applications in Medicine: A Transformative Leap
• Dark Web Surveillance: Ethical Dilemmas and Technological Advances
• Biological Computing: Decoding the Potential for Future Technologies

Simple Technology Research Paper Topics

• Navigating Privacy in Social Media Platforms
• Drones in Delivery Services: Efficiency versus Safety
• Data Encryption: An Essential in Modern-Day Communication
• Applications and Challenges of Chatbots in Customer Service
• Neural Networks: Unraveling the Complexity
• Voice Recognition Technology in Smart Devices
• Mobile Technology: Changing the Face of E-Commerce
• Gamification in E-Learning Platforms
• Internet of Things (IoT): Redefining Home Automation
• Cloud Computing: Understanding the Pros and Cons
• Bluetooth Technology: An Analysis of Connectivity Issues
• 3D Printing: A Revolution in Manufacturing
• Virtual Reality: A Game Changer for the Gaming Industry
• Light Fidelity (Li-Fi): An Alternative to Wi-Fi
• Understanding Cryptography in Blockchain
• Advancements in Facial Recognition Systems
• E-Waste Management: Technological Solutions
• Artificial Intelligence: Decoding Its Myths and Realities
• Electronic Voting Systems: Security Concerns

Interesting Technology Research Paper Topics

• Intricacies of Quantum Cryptography: A Closer Look
• Bridging the Digital Divide: Technology in Rural Education
• Machine Learning and Predictive Analysis: Unseen Patterns
• Reality Mining: Exploring Data From Social Interactions
• Smart Cities: Prospects and Pitfalls
• Augmented Humans: Exploring Biohacking Techniques
• Nanobots in Medicine: A Future Perspective
• Interplay Between Social Media Algorithms and User Behavior
• Predictive Policing: Merits and Ethical Dilemmas
• Internet of Things (IoT) in Disaster Management
• Biometric Technology in Immigration: Assessing Effectiveness
• Autonomous Weapons: Ethical Implications and Control Measures
• Forensic Applications of DNA Sequencing Technology
• Space Tourism: Technological Challenges and Future Prospects
• Machine Learning in Stock Market Predictions
• Blockchain in Digital Identity Verification
• Cognitive Radio: Optimizing Spectrum Use
• Risks and Rewards of Cyber-Physical Systems
• Big Data in Genomics and Personalized Medicine
• Food Technology: Innovations for Sustainable Diets

Technology Research Topics for College Students

• Smart Fabrics: Merging Fashion With Technology
• Wireless Power Transfer: Understanding its Feasibility
• Artificial Intelligence in Personal Finance: An Emerging Trend
• Understanding Cybersecurity Vulnerabilities in IoT Devices
• Bioinformatics: Decoding the Data of Life
• Nano-Bio Technology: Applications in Health and Environment
• Augmented Reality in Tourism: A New Era of Exploration
• Neural Networks in Image Processing: A Detailed Study
• Hydroponics and Vertical Farming: Technology for Urban Agriculture
• Challenges and Solutions in E-Waste Recycling
• Brain-Computer Interfaces: The Future of Neurological Therapies
• 3D Bioprinting: Revolutionizing Transplant Medicine
• Big Data in Sports Analytics: Changing the Game
• Haptic Technology: Enhancing the Virtual Reality Experience
• Understanding the Potential of Quantum Sensors
• Green IT: Sustainable Practices in Technology Companies
• Digital Forensics: Tools and Techniques for Cyber Crime Investigation
• Solar Power Technology: Innovations for a Greener Future
• Digital Watermarking: Applications in Media and Art

Technology Research Topics for University

• Blockchain and Healthcare: Ensuring Data Privacy
• Fusion Energy: Understanding Technological Challenges
• Gene Editing Technology: Implications for Human Health
• Intrusion Detection Systems in Cybersecurity: An Evaluation
• Artificial Intelligence in Climate Change Modelling
• Wireless Sensor Networks in Environmental Monitoring
• Digital Twins: Facing the Gap Between Physical and Virtual
• Internet of Nano Things (IoNT): A Look Into the Future
• Quantum Computing and Post-Quantum Cryptography
• Exploring the Applications of Holographic Technology
• Machine Learning in Predicting Disease Outbreaks
• Autonomous Drones in Search and Rescue Operations
• Understanding the Mechanism of Neural Implants
• Smart Packaging: The Future of Food Safety
• Analyzing the Potential of Perovskite Solar Cells
• Digital Accessibility: Overcoming Barriers in Technology
• Molecular Computing: An Alternative to Silicon-Based Computers
• 5G Technology: Exploring the Cybersecurity Implications
• Augmented Reality in Structural Design and Architecture
• Plastic Recycling Technology: An Approach Toward Circular Economy

Technologies & Computer Science Research Topics

• Harnessing Quantum Entanglement in Secure Communication
• Advancements in Distributed Systems: A Deeper Look Into Edge Computing
• Understanding and Overcoming Challenges in Deep Learning Optimization
• Artificial Intelligence in Drug Discovery: Techniques and Limitations
• In-Depth Analysis of Probabilistic Graphical Models
• Algorithmic Fairness and Transparency in Machine Learning
• Biocomputation: Exploring the Frontier of Molecular Machines
• Emerging Techniques in Non-Volatile Memory Systems
• Application and Limitations of Homomorphic Encryption in Cloud Computing
• Internet of Things (IoT): Addressing the Scalability Issues
• Designing Energy-Efficient Architectures for High-Performance Computing
• Exploring the Efficacy of Multi-Objective Evolutionary Algorithms
• Nano-Scale Communication: Challenges in Network Design
• Bayesian Deep Learning: Bridging Uncertainty and Complexity
• Development of Sustainable Cryptocurrencies: A Technological Perspective
• Interpretable Machine Learning: Making AI Transparent and Accountable
• Analyzing the Security Measures in Next-Generation 6G Networks
• Computer Vision and Image Understanding: Advanced Techniques and Applications
• Advanced Intrusion Detection Systems in Cybersecurity: New Approaches
• Quantum Machine Learning: Convergence of Quantum Computing and AI

Artificial Intelligence Technology Research Topics

• Explainable AI: Techniques for Improving Transparency
• Neurosymbolic Computing: Bridging the Gap Between Neural and Symbolic Networks
• Artificial General Intelligence: Feasibility and Challenges
• Reinforcement Learning: Novel Approaches for Reward Function Design
• Machine Ethics: Incorporating Human Values Into Autonomous Systems
• Adversarial Attacks on Deep Learning Systems: Mitigation Techniques
• Automated Machine Learning: Improving Efficiency of Model Development
• Emotion AI: Building Empathetic Machines
• Developing Robustness in AI Systems: A Study on Uncertainty Quantification
• Multimodal Learning: AI Understanding of Integrated Sensory Data
• AI Governance: Frameworks for Ethical Machine Decision-making
• Natural Language Processing: Advances in Contextual Understanding
• Generative Models: Novel Applications and Challenges in AI Artistry
• Understanding AI Bias: Techniques for Fair Algorithmic Practices
• Swarm Intelligence: Inspirations From Nature for Problem-Solving AI
• Human-AI Collaboration: Enhancing Synergy in Mixed Teams
• Machine Vision: Next-Gen Innovations in Image Recognition
• Transfer Learning: Maximizing Efficiency in AI Training
• Artificial Creativity: Understanding the Mechanisms of AI in Art and Design

Video Gaming Technology Research Topics

• Game Physics: Realism and Computation Trade-Offs
• Procedural Generation: Advanced Techniques in Game Design
• Development of Next-Generation Gaming Consoles: A Technical Perspective
• Deep Learning in Video Game AI: Emerging Trends
• Haptic Feedback Technology: Enhancing User Experience in Virtual Reality Games
• Exploring the Limitations of Cloud Gaming Technology
• Player Behavior Modeling: Machine Learning Applications in Multiplayer Games
• Use of Ray Tracing in Real-Time Rendering: Technical Challenges
• Neurogaming: Merging Neuroscience With Video Game Technology
• Audio Techniques in Immersive Gaming: A Comprehensive Study
• Augmented Reality Gaming: Future Prospects and Challenges
• AI-Driven Game Design: Automating the Creative Process
• Virtual Reality Motion Sickness: Understanding and Addressing the Problem
• Cybersecurity in Online Gaming: Protecting Against Emerging Threats
• Biofeedback in Gaming: Personalizing the Player Experience
• Esports and AI: Improving Training and Performance Analysis
• Next-Level Gaming: Exploring the Potential of Quantum Computing
• Blockchain Technology in Gaming: Opportunities and Challenges
• Cross-Platform Gaming: Technical Hurdles and Solutions
• Spatial Computing: The Future of Augmented Reality Games

Educational Technology Research Topics

• Integration of Augmented Reality in Classroom Learning
• Adaptive Learning Systems: Tailoring Education to Individual Needs
• Exploring the Efficacy of Digital Game-Based Learning
• Artificial Intelligence in Personalized Education: Scope and Challenges
• Serious Games: Assessing their Potential in Education
• Implementing Cybersecurity Education in School Curricula
• Effectiveness of Mobile Learning in Diverse Educational Settings
• Learning Analytics: Enhancing Student Success With Big Data
• Virtual Reality in Special Education: Overcoming Barriers
• Applying Natural Language Processing in Automatic Essay Grading
• Developing Open-Source Educational Software: Challenges and Opportunities
• E-Learning: Identifying Optimal Strategies for Adult Education
• Technological Approaches for Inclusive Education
• Blockchain in Education: A Study on Records Management
• Harnessing the Power of AI in STEM Education
• Flipped Classroom Model: Evaluating its Effectiveness With Technology
• Immersive Learning Environments: The Role of Virtual Reality
• Collaborative Learning in Online Education: Technological Tools and Strategies
• Machine Learning Applications in Predicting Student Performance
• Exploring the Intersection of Neuroscience and EdTech

Biotechnology Research Topics

• Harnessing CRISPR Technology for Precision Medicine
• Synthetic Biology: Developing Novel Biological Systems
• Genome Editing: Ethical and Safety Considerations
• Nanotechnology in Drug Delivery: Prospects and Challenges
• Tissue Engineering: Innovations in Regenerative Medicine
• AI Applications in Genomics: Exploring Potential and Limitations
• Pharmacogenomics: Personalizing Medicine With Genetics
• Therapeutic Applications of Stem Cell Technology
• Microbiome Research: Implications for Human Health
• Gene Therapy: Advanced Techniques and Challenges
• Biomaterials in Medical Implants: A Comprehensive Study
• Bioinformatics in Disease Prediction: Latest Approaches
• Cellular Agriculture: The Science Behind Lab-Grown Meat
• Microbial Fuel Cells: Biotechnology in Sustainable Energy
• Molecular Diagnostics: Innovations in Pathogen Detection
• Bioprinting: 3D Printing of Organs and Tissues
• Nanobiosensors: Early Disease Detection Techniques
• Proteomics: Advanced Technologies and Their Applications
• DNA Data Storage: Understanding the Feasibility and Challenges

Communications and Media Technology Research Topics

• Network Function Virtualization: Innovations and Challenges
• Deep Learning Algorithms in Automated Journalism
• 5G Wireless Technology: Overcoming Implementation Hurdles
• Digital Broadcasting: Exploring the Future of Television
• Artificial Intelligence in Media Production: Potential and Limitations
• Blockchain Applications in Digital Rights Management
• Internet of Things: Enhancing Smart Home Connectivity
• Satellite Communication: New Frontiers in Space-Based Networks
• Quantum Cryptography in Secure Communication
• 3D Holography: Future of Telecommunication
• AI-Driven Media Personalization: Ethical Considerations
• Optical Fiber Technology: Enhancing Global Connectivity
• Social Media Analytics: Leveraging Big Data
• Next Generation Networks: Preparing for 6G Wireless Communication
• Human-Computer Interaction: Advancements in Conversational AI
• Deepfake Technology: Assessing Societal Implications and Countermeasures
• Immersive Journalism: Leveraging VR in News Reporting
• AI in Content Moderation: Efficiency and Accuracy Trade-Offs
• Data Compression Techniques: Innovations for Efficient Storage
• Digital Forensics: Advanced Techniques for Media Analysis

Energy Technologies Research Topics

• Harnessing Tidal Power: Advances in Marine Energy
• Fusion Energy Technology: Exploring the Challenges
• Nanotechnology in Solar Cells: Efficiency Enhancement Methods
• Hydrogen Fuel Cells: Overcoming Technological Hurdles
• Geothermal Energy: Innovations in Power Generation
• Artificial Photosynthesis: A Sustainable Energy Solution
• Thermoelectric Materials: Converting Waste Heat Into Power
• Wireless Power Transmission: Assessing Feasibility and Efficiency
• Smart Grids: Incorporating AI for Energy Management
• Carbon Capture Technologies: Solutions for Climate Change
• Biofuels: Advanced Techniques in Renewable Energy
• Solid-State Batteries: Future of Energy Storage
• Energy Harvesting: Utilizing Ambient Energy Sources
• Next-Generation Nuclear Power: Advancements in Reactor Designs
• Grid Energy Storage: Addressing Intermittency in Renewable Power
• Perovskite Solar Cells: Investigating Stability and Performance
• Wind Energy: Exploring Offshore and Floating Turbines
• Thermochemical Storage: Solutions for Seasonal Energy Storage
• Concentrated Solar Power: Technological Advances and Challenges

Food Technology Research Topics

• Precision Fermentation: Innovations in Food Production
• Edible Packaging: Exploring Sustainable Solutions
• Artificial Intelligence in Food Quality Control
• Food Fortification: Enhancing Nutrient Bioavailability
• Cultured Meat: Technological Challenges and Opportunities
• Microbial Biotechnology in Fermented Foods
• Nanotechnology Applications in Food Preservation
• 3D Food Printing: Potential and Limitations
• Insect Farming: A Sustainable Protein Source
• Smart Farming: AI in Crop Management and Disease Detection
• Food Traceability: Applications of Blockchain
• Nutrigenomics: Personalized Nutrition Based on Genetics
• Active and Intelligent Packaging: Enhancing Food Safety
• Aquaponics: Sustainable Solutions for Urban Farming
• Food Waste Management: Advanced Biotechnological Approaches
• High-Pressure Processing: Enhancing Food Shelf Life
• Synthetic Biology: Developing Novel Flavors and Textures
• CRISPR Technology in Crop Breeding
• Functional Foods: Advances in Probiotics and Prebiotics
• Bioactive Peptides: Extraction Techniques and Health Benefits

Medical Technology Research Topics

• Innovations in Medical Imaging: Exploring the Potential of AI
• Telemedicine: Addressing Barriers to Adoption
• 3D Bioprinting: A New Frontier in Regenerative Medicine
• Neuroprosthetics: Advances in Brain-Computer Interfaces
• Genetic Testing: Navigating Ethical, Legal, and Social Issues
• Health Informatics: Applying Big Data to Improve Patient Outcomes
• Nanomedicine: Progress and Challenges in Targeted Drug Delivery
• Wearable Technology: Enhancing Patient Monitoring
• Robot-Assisted Surgery: Evaluating Effectiveness and Patient Safety
• Artificial Organs: Developments in Bioartificial Technology
• Precision Medicine: Integrating Genomics Into Healthcare
• Remote Patient Monitoring: The Future of Chronic Disease Management
• Virtual Reality in Pain Management: Investigating Efficacy
• Cybersecurity in Healthcare: Safeguarding Patient Data
• CRISPR in Disease Treatment: Examining the Potential of Gene Editing
• AI in Predictive Analysis: Anticipating Disease Outbreaks
• Smart Pills: Revolutionizing Drug Delivery and Diagnostic Capabilities
• Machine Learning in Medical Diagnosis: Limitations and Possibilities
• Biomedical Optics: Advanced Imaging for Early Cancer Detection
• Brain Implants: Investigating the Potential for Memory Enhancement

Pharmaceutical Technologies Research Topics

• Enhancing Bioavailability in Drug Delivery With Nanotechnology
• Pharmacogenomics: Personalizing Medication Regimens
• Gene Therapy: Overcoming Delivery and Safety Challenges
• Biologics: Advances in Production and Purification Techniques
• AI in Drug Discovery: Speeding Up the Process
• Protein Engineering: Designing Next-Generation Therapeutics
• 3D Printing of Pharmaceuticals: Customization and Precision Dosing
• CRISPR: Opportunities for Novel Drug Development
• Pharmaceutical Formulation: Advances in Controlled Release Systems
• Pharmacokinetics and Pharmacodynamics: Modern Computational Approaches
• Neuropharmacology: Understanding the Blood-Brain Barrier for Drug Delivery
• Microfluidics in Drug Discovery: High-Throughput Screening Methods
• Advanced Biosensors for Drug Level Monitoring
• Antibody-Drug Conjugates: Balancing Efficacy and Safety
• Smart Drug Delivery Systems: Responsive and Targeted Approaches
• Machine Learning in Predicting Drug Interactions
• Bioequivalence Studies: New Approaches for Complex Drug Products
• Pharmaceutical Biotechnology: Developments in Therapeutic Proteins
• Nanoparticles in Vaccine Development: Innovations and Challenges

Transportation Technology Research Topics

• Autonomous Vehicles: Navigating the Road to Full Autonomy
• Hyperloop Technology: A Future Transportation Solution?
• Electric Aircraft: Challenges in Battery Technology and Infrastructure
• Maritime Drones: Applications and Challenges in Oceanic Transport
• Smart Traffic Management: AI Solutions for Urban Congestion
• Connected Vehicles: Cybersecurity Considerations and Solutions
• Magnetic Levitation (Maglev) Trains: Exploring Technological Advances
• Intelligent Transportation Systems: Evaluating the Role of IoT
• Sustainable Maritime Transport: Opportunities for Green Ships
• Aerodynamics in Vehicle Design: Energy Efficiency Strategies
• Air Taxis: Investigating Feasibility and Infrastructure Needs
• Digital Twins in Transportation: Improving Maintenance and Predicting Failures
• Hydrogen Fuel Cells for Transportation: Overcoming Technological Hurdles
• AI in Public Transportation: Optimizing Routes and Schedules
• Cargo Bikes: Assessing their Potential in Urban Freight Transport
• Battery Technology for Electric Vehicles: Future Prospects
• High-Speed Rail Networks: Exploring Economic and Environmental Impact
• Unmanned Aerial Vehicles: Regulations and Safety Measures
• Space Tourism: Technological Challenges and Prospects
• Self-Healing Materials: Innovations in Road and Infrastructure Maintenance

High-Quality Technology Research Topics

• Cybersecurity in Quantum Computing: Protecting the Future
• Blockchain Applications Beyond Cryptocurrency
• Machine Learning in Astrophysics: Uncovering Cosmic Mysteries
• AI-Driven Climate Change Models: Enhancing Predictive Accuracy
• Advanced Robotics: Exploring Humanoid Potential
• Genetic Algorithms: Solutions for Optimization Problems
• Nanotechnology in Environmental Remediation: Promise and Challenges
• Dark Web: Investigating Patterns and Anomalies
• Neural Networks in Weather Prediction: Optimizing Models
• Smart Homes: AI in Domestic Energy Management
• Quantum Teleportation: Exploring Real-World Applications
• Exoskeletons: Advances in Wearable Robotics
• Internet of Things (IoT) in Agriculture: Precision Farming Solutions
• Photonics: Innovations in Optical Computing
• Underwater Wireless Communication: Technological Challenges
• Smart Dust: Applications and Ethical Concerns
• Biometric Authentication: Enhancing Security in the Digital Age
• Mixed Reality in Education: Potential and Limitations
• Swarm Robotics: Coordinated Autonomy and Applications

Informative Technology Research Topics

• Information Security: Addressing Emerging Cyber Threats
• Blockchain Technology: Beyond Bitcoin and Cryptocurrencies
• Digital Forensics: Unveiling Cyber Crime Investigations
• Cloud Computing: Data Privacy and Security Concerns
• Data Visualization: Enhancing Decision-Making With Interactive Graphics
• Internet of Things: Smart Homes and Their Privacy Implications
• Artificial Intelligence in Healthcare: Automating Diagnosis
• Quantum Computing: Future Scenarios and Challenges
• Social Media Analytics: Understanding Consumer Behavior
• Virtual Reality: Applications in Mental Health Therapy
• Augmented Reality in Retail: Changing the Shopping Experience
• Machine Learning: Improving Weather Forecast Accuracy
• Cyber-Physical Systems: The Backbone of Industry 4.0
• Deep Learning: Enhancements in Image Recognition
• Digital Twin Technology: Applications in Manufacturing
• Neural Networks: Enhancing Language Translation Systems
• Big Data Analytics: Overcoming Processing Challenges
• Edge Computing: Handling IoT Data Closer to the Source
• Cryptocurrency Regulations: Balancing Innovation and Security

Lucrative Technology Research Topics

• Artificial Intelligence in Stock Market Predictions: Accuracy and Profits
• Fintech Innovations: Disrupting Traditional Banking
• Big Data in E-commerce: Driving Sales and Customer Satisfaction
• Blockchain Technology: Applications in Supply Chain Management
• Cloud Computing: Revenue Generation in the Software Industry
• Internet of Things: Business Opportunities in Smart Home Technologies
• Cybersecurity Services: A Growing Market in the Digital Age
• Machine Learning: Enhancing Profitability in Digital Advertising
• Virtual Reality in Real Estate: Boosting Sales Through Immersive Experiences
• Robotic Process Automation: Cost Savings in Business Operations
• Biometric Technology: Revenue Opportunities in Security Systems
• 5G Technology: Impact on Telecommunications Industry Revenue
• E-Waste Recycling: Profitable and Environmentally Friendly Solutions
• AI Chatbots: Customer Service Cost Reduction
• Health Informatics: Profitability in Healthcare Data Management
• Cryptocurrencies: Financial Opportunities and Risks
• Digital Twin Technology: Revenue Generation in Industrial Applications
• EdTech Innovations: Business Opportunities in Online Education
• Wearable Tech: Profitability in the Fitness Industry
• Data Science Consulting: Lucrative Opportunities in Business Intelligence

Outstanding Technology Research Topics

• Artificial Intelligence in Climate Change: Predictive Models and Solutions
• Blockchain Technology: Enhancing Food Traceability
• Quantum Computing: Breaking New Ground in Cryptography
• Augmented Reality: Changing the Landscape of Tourism
• Virtual Reality in Pain Management: Emerging Therapeutic Approaches
• Machine Learning in Wildlife Conservation: Species Identification and Tracking
• Neural Networks: Improving Seismic Data Interpretation
• Internet of Things: Smart Farming and Precision Agriculture
• Cloud Computing: Opportunities in Healthcare Data Storage and Analysis
• Genomic Data Analysis: Unraveling Complex Biological Systems
• Autonomous Vehicles: A Deep Dive Into Lidar Technologies
• 5G Networks: Enabling Next-Generation IoT Devices
• Cybersecurity: AI-Driven Solutions for Advanced Persistent Threats
• Green Data Centers: Energy Efficiency and Sustainability Practices
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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Technology and the Environment Research Paper

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I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with 24start discount code, ii. early work linking technology and the environment with human social organization, iii. impact: considering humanity’s effects on the environment, a. water pollution, b. soil erosion, depletion, and unsustainable agriculture, c. declining biodiversity, d. deforestation, e. global warming, iv. considering the primary human causes of environmental impact, a. technology, b. population, c. affluence, inequality, and consumption, v. looking ahead as society moves through the 21st century, vi. conclusion.

Throughout time, humanity has grappled with questions of how to survive and, in so doing, to meet the needs for basics such as food and shelter. Historically, humankind has used technology to assist in the pursuit of these survival basics. Researchers examining society from a comparative and historical perspective note that as subsistence technology has developed—for example, from the digging stick to the plow to the steam engine—so have there been profound changes in the ways societies themselves are organized (e.g., Lenski 1966; Lenski and Nolan 1984.

With the advance in technology, societies are able to acquire and produce more food and to accumulate surpluses. This leads to a number of profound changes in social and ecological processes, including changes in the numbers of people living in a society, and, more generally, on the planet, and in the patterns of accumulation and distribution of resources among those people. Furthermore, as technology allows for deeper incursions into the earth, the potential for environmental impact increases dramatically (Ponting 1991; McNeill 2000).

Because of the profound implications for the well-being, and perhaps even the long-term survival, of humanity, questions about interactions of social arrangements among human beings, the technologies they produce, and their impacts on the natural environment are vitally important to sociologists. Yet by their very nature, these questions involve a number of aspects, and as such, their study typically has been interdisciplinary. The study of social-technological-environmental interactions, by its very nature, draws on a number of subfields. We now turn to some of the attempts to bring social scientific analysis to these questions.

Some of the early attempts to examine these interrelationships were undertaken by sociologists, but with a heavy influence of other disciplines, most notably biology. These came to be known under a broad rubric of human ecology (e.g., Duncan 1964; Commoner 1971, 1992; Catton 1980, 1994; Catton and Dunlap 1978; Hawley 1981).

Human ecologists developed a framework that came to be known as the POET model, so named because of the acronym formed by the four major variables: population (human social), organization, environment, and technology. While this model served as a useful way to focus discussions about human-environmental interactions, it was not particularly influential in guiding empirical research. One of the chief criticisms spoke to the ecological nature of the model itself, in that it did not specify an outcome and did not make specific predictions (for an in-depth discussion, see Dietz and Rosa 1994).

As sociologists and others came to recognize the limitations of the POET model, it was modified by a number of researchers around several emerging themes. A series of arguments were advanced that a set of models should be specified that could predict environmental impacts, such as deforestation, greenhouse gas emissions, and air and water pollution. As a very general way of conceptualizing the problem, environmental impact was seen as being a function of population, technology, and human consumption levels (which came to be referred to in many of the models as “affluence” because of the high correlation in many societies between levels of wealth and patterns of consumption). They presented the IPAT model, in which (Environmental) Impact = Population * Affluence * Technology (Ehrlich 1968; Commoner 1971, 1992; Ehrlich and Ehrlich 1981, 1990; Dietz and Rosa 1994).

Each of the four terms can be defined in a number of ways, and as such, the IPAT model should be seen as a general framework rather than a specific predictor (Dietz and Rosa 1994). For example, while some of the same social factors that are linked with an environmental impact, such as greenhouse gas emissions, can also be used to predict deforestation, there are important differences as well. While population dynamics are important to consider in predicting environmental impact, specifics about population distributions are often more informative than overall levels of population. Studies, for example, show that rural population growth is much more closely associated with deforestation, while urban population growth is more closely associated with greenhouse gas emissions and levels of resource consumption (Burns, Kick, and Davis 1997, 2003).

Furthermore, the social factors most closely associated with what predicts one greenhouse gas (carbon dioxide) differ in important ways from those predicting another greenhouse gas (methane) (Burns et al. 1994; Burns, Kick, and Davis 1997; Jorgenson 2006). Much of the work has followed in this vein, and in a notable variant, researchers have reformulated the IPAT approach into the STIRPAT model, an acronym for Stochastic Impacts by Regression on Population, Affluence, and Technology (e.g., York, Rosa, and Dietz 2003). While all the specifics of these processes are beyond the purview of this entry, it is nonetheless important to realize that such distinctions as to the scope of precise causes of particular environmental impacts are important for researchers and policymakers to consider. Attention to such detail can often lead to insight about why there are findings that may be characterized as “conflicting” in the popular press. It is thus important to give detailed attention to each of the respective areas of the overall framework, as well as to the overall picture.

In developing countries, approximately 90 percent of human sewage is simply dumped without any attempt at treatment whatsoever (World Resources Institute 1996:71). These discharges often go directly into water; yet even when the dumping is not direct, it often leaches into underground aquifers. Either way, this causes serious pollution problems and the public health risks associated with them. While adequate supplies of safe drinking water become more imperiled worldwide, it is a particularly acute problem in parts of the developing world where population growth is outstripping the local resources. By the most reliable estimates, for instance, by the year 2025, at least a billion people in northern Africa and the Middle East will lack water for basic necessities like drinking and sustaining their crops (Postel 1993).

Runoff of water contaminated by short-sighted farming practices, such as indiscriminate use of synthetic fertilizers, pesticides, and herbicides, as well as from concentrations of livestock animal waste from huge feed lots leads to a number of ecological and health problems, particularly for those living downstream from them (Steingraber 1998; Burns, Kentor, and Jorgenson 2003).

On average, farmland in the United States now has only about two-thirds as much topsoil as it did at the beginning of the nineteenth century (Pimentel et al. 1995). This is directly attributable to poor land management practices, such as raising one crop over large stretches of land (monocrop agriculture) and the extensive use of tractor plows and synthetic fertilizers and pesticides. Typically, this leads to a situation in which soil is either blown away by wind or washed away by rain or by irrigation. Only on about 10 percent of U.S. farmland is soil being replaced as fast as it is being eroded, typically through the slow but rich process of naturally breaking down organic matter (Pimentel et al. 1995).

Historically, societies expanded their food production by increasing the amount of land dedicated to farming and grazing. This worked well as long as there was fertile soil that could be brought under cultivation. However, these increases are necessarily bound by the amount of total land available to a society, and ultimately by the size of the planet. Over time, only less fertile land was available, and people increasingly began to attempt cultivating land that needed something beyond what was available through the natural environment to produce food.

As Rachel Carson noted as early as 1962 in her landmark work The Silent Spring, a number of chemicals the U.S. Army developed under wartime conditions during World War II became generally available to farmers at the end of that war. These included herbicides and pesticides such as DDT, as well as synthetic fertilizers. Already by the 1950s, these had come into widespread use, particularly in developed countries (Brown, Flavin, and Kane 1992).

Since about 1980, the amount of land dedicated to farming has actually been decreasing for the first time in history; this trend is particularly strong in developed countries (Pimentel 1992). While it is true that greater amounts of food can be produced in the short run by the use of monoagriculture, pesticides, herbicides, and synthetic fertilizers, in the longer run, this leads to soil erosion and degradation.

The earth and its subregions are in a delicate ecological balance. Loss of a species leads to a number of problems, not the least of which is that the fragile balance often gets upset, sometimes leading to catastrophic results (Ryan 1992). For example, in the 1920s the people in Kern County, California, decided to eliminate threats to their crops and livestock. They killed every such threat they could find—skunks, coyotes, snakes, foxes, and beavers. For their efforts, they were repaid by being overrun by millions upon millions of mice, in what was (at least to date) the worst rodent infestation in U.S. history (Maize 1977, cited in Eisenberg 1998).

By some estimates, anywhere from 15 to 75 species in tropical rainforests go extinct on an average day (Ehrlich and Ehrlich 1981; Wilson 1990, 1992). Yet many of the “miracle drug” cures come from plants (many of them teetering on the edge of extinction) in those very rainforests (Soejarto and Farnsworth 1989).

The major social causes of deforestation involve population dynamics, the level and growth of economic development, and the structure of international trade (e.g., Rudel 1989; Kick et al. 1996; Lofdahl 2002; Burns, Kick, et al. 2003). However, changing technologies greatly affect all three of these major causes in different ways, meaning that technology affects deforestation indirectly and has done so throughout human history (e.g., Chew 2001; Diamond 2005).

The effects of population are often addressed in the context of urban population growth and rural population growth. For example, rural population growth increases the likelihood that forested regions will be transformed, cut, or burned for use in industrial activities, extractive processes, or agricultural production, and related technological developments only exacerbate the environmental impacts of these activities (Rudel 1989; Burns et al. 1994; Rudel and Roper 1997).

Rudel (1989) and Ehrhardt-Martinez (1998) argue that economic development in less developed countries will increase deforestation by expanding the availability of capital for productive ventures in extractive industries and agriculture (for further discussion, see Marquart- Pyatt 2004). Conversely, Burns, Kick, et al. (2003) find that the least developed countries experience the highest rates of deforestation, followed by middle-developed countries, and highly developed ones sometimes experience attempts at reforestation. This pattern can be attributed, at least in part, to a process of recursive exploitation, in which environmental resources of the least developed countries are acquired at a discount by entrepreneurs and corporate actors from both highly developed and developing nations, while the resources of developing countries accrue primarily to actors in highly developed countries (e.g., Burns, Kick, et al. 2003, 2006; Burns, Kick, and Davis 2006).

In a related vein, higher-consuming countries partially externalize their consumption-based environmental costs to less developed countries, which increases deforestation within the latter (see also Jorgenson and Rice 2005). This externalization largely takes the form of the flow of raw materials and produced commodities from less developed to more developed countries, and technological developments in extractive and productive sectors as well as transport (e.g., shipping) intensify the environmental degradation associated with these asymmetrical international exchanges (e.g., Bunker 1984; Jorgenson and Rice 2005).

The human dimensions of climate change and global warming are perhaps the most widely addressed human-environment relationships in the social sciences and policy venues. There is general consensus in the scientific community that global warming is indeed a reality and that human societies do contribute to the warming of the earth’s atmosphere through activities that lead to the emission of noxious greenhouse gases (National Research Council 1999). Atmospheric greenhouse gases absorb and reradiate infrared energy and heat back to the earth’s surface, which increases water, land, and air temperatures in the biosphere (Christianson 1999).

Two of the most serious greenhouse causing gases emitted into the atmosphere as a by-product of human activity are carbon dioxide and methane. In terms of scale, carbon dioxide accounts for the largest volume of greenhouse gas caused by humans; molecule for molecule, methane is an order of magnitude more effective at absorbing and reradiating infrared energy and heat back to the earth’s surface. The primary human activity contributing to carbon dioxide emissions is the use of fossil fuels. Methane emissions are increased by the refining of fossil fuels as well as through increased cattle production and large-scale agriculture activities, particularly the growing of rice (Jorgenson 2006).

With technological development comes the ability to dig deeper, to go farther into the earth, oceans, and space. While this allows people to produce more food, clothing, shelter, and luxury items, it also makes greater demands on the world’s resources and dramatically increases the accumulation of waste products.

Some analysts argue that the earth is robust enough to cope with waste products and will regenerate itself (e.g., Simon 1983, 1990; Simon and Kahn 1984; for counterarguments, see Ehrlich and Ehrlich 1981, 1990). While almost anything will be broken down and recycled by the natural environment, the question of how long this will take is crucial. For example, a single glass bottle can be broken down, but the process takes about 10,000 years. The use of technology in allowing people to extract resources and then to use them in increasingly exotic combinations has the potential to lead society to the point where the earth will not be able to regenerate itself in time for the human race to live and use technology in the way it does (Ehrlich and Ehrlich 1981).

Technology is most readily available in core societies, but it is also becoming increasingly widespread throughout the world, especially in rapidly developing countries. It is true, however, that if environmental regulation is promulgated at all, it tends to be done primarily in the high-consuming, developed societies. Thus, the developing societies often have a combination of technology with a lack of concomitant regulation. The result is that the developing societies are often places with some of the worst ecological degradation.

The former U.S. Vice President Gore (1993), for example, gives a tragic illustration of some of the social dynamics behind the Aral Sea drying up—a sea that had been the fourth largest landlocked body of water in the world and that had provided a livelihood for thousands of people. A number of factors contributed to this, not the least of which was an irrigation system that had been used to grow cotton in an otherwise desert climate. The cotton was grown originally for economic reasons—it could draw a better price on the world market than virtually anything else that could be grown there, but only in the short run. In the long run, the diversion of water effectively changed the hydrological cycle in that area. Once the hydrological cycle is changed, it is often changed permanently.

In this case, the technology was sophisticated enough to change the natural ecology in a dramatic way. This was done as a short-term response to economic pressures for survival in an increasingly competitive world. There was another component to the problem as well: With the dissolution of the Soviet Union, the Aral Sea was no longer entirely in one state (it was in a part of two contiguous newly created states, Uzbekistan and Kazakhstan). The technological sophistication was not matched by environmental regulation.

If technology can be used to destroy the earth, could it also be used to help repair it? There are a number of beneficial uses of technology, and certainly, technological development can, if done with its environmental consequences in mind, harness some of those benefits. Ecologically sound energy sources, such as solar and wind power, are not currently in a state of development that enables them to compete with fossil fuels under current market conditions. However, with more research, it may well be that these ecologically sound energy sources become generally available.

Some theorists, most notably Julian Simon and his collaborators (e.g., Simon 1983, 1990; Simon and Kahn 1984), hold that technological development will help to alleviate society’s most pressing problems. Most notably, Simon believes that environmental problems will, given enough technology, be overcome. In fact, Simon and his collaborators criticize Malthus ([1798] 1960) and his followers as well. Simon believes that increasing population size will lead to increasing levels of human interaction and, thus, the much greater probability that some of those people will develop critically needed technology.

Consider, too, that the internal combustion automobile, one of the greatest polluters of all time, was originally welcomed as a clean alternative to the pollution caused by horses in city streets. There is an important lesson here. Human actions, including the production and use of technological innovation, almost always have unforeseen or unintended consequences. Nobody develops a technology deliberately to pollute, yet pollution is often a consequence of technology. This is not to say that society should cease trying to develop technologically. Rather, we would do well to approach technology with enough humility to recognize that we cannot always control the outcome and that continually relying on technology to solve environmental problems may be flirting with disaster.

As of the beginning of the third millennium, there are over 6 billion people in the world, and that number is rising rapidly. Most of the very rapid population increases have taken place since the advent of the industrial revolution and the technological advances associated with it. Consider that the world population mark surpassed only 1 billion in about 1850 AD. According to United Nations projections, by the year 2025, that number will be up to 8 billion (United Nations Population Division 1995).

Over two centuries ago, Thomas Malthus ([1798] 1960) noted that the technological progress associated with the beginning of the industrial revolution had a number of consequences for the human race. Malthus thought that with the increasing capacity of production, there would be a tendency for population to increase dramatically. While Malthus saw the ability of society to produce the necessities of life, such as food, clothing, and shelter, as increasing linearly (what he termed “arithmetically”), this would lead people to have many more children, and so the population would increase exponentially (what Malthus termed “geometrically”). The mismatch between the modest growth in the ability to produce resources and the tremendous growth in the size of the population would eventually lead to “overpopulation”; this term that Malthus coined— overpopulation—has been part of human dialogue ever since.

More specifically, Malthus argued that overpopulation and the problems associated with it, such as severe crowding and competition for scarce resources, would eventually lead to serious social problems. Recalling the Apocalypse, or the last book of the Bible, Malthus theorized that overpopulation would lead to its own “four horsemen” of the apocalypse. For Malthus, the four horsemen were war, famine, plague, and pestilence. Malthus has inspired a number of modern-day thinkers, who also see population growth as the central cause of a plethora of social and environmental problems (e.g., Ehrlich 1968; Ehrlich and Ehrlich 1990; Abernethy 1991; Bongaarts 1994; Pimentel et al. 1994; Cohen 1995; see also United Nations Population Fund 1991, 1997, 1999).

While absolute size of the population is crucial, distribution of the population is important as well (Burns et al. 1994; Burns, Kick, and Davis 2006). Dramatic increases in population, particularly in rural areas, often lead to serious environmental degradation in those areas, as people clear previously forested land, for example.

While the world’s population is increasing, and is now over 6 billion people, the greatest population increases are in the least developed countries. Unless resources can be increased (through, e.g., technological advance), the proportion of resources accruing to any given person, especially in the countries that are already the poorest in the world, will likely decrease over time.

While no one knows for sure the precise carrying capacity of the planet, there are a number of trade-offs that eventually must be made. One such trade-off, ultimately, may be a quantity/quality one, in which the planet may support, for example, a population of upward of 10 billion people but at a lifestyle greatly diminished from what is currently the case, especially in developed, mass-consumption-oriented societies (Cohen 1995).

Historically, the more developed a society, the greater the urbanization of that society. A century ago, for example, virtually all the major cities of the world were in developed countries. Over time, however, particularly in the late twentieth and the twenty-first centuries, the rapidly developing countries, such as India and Mexico, have been urbanizing very rapidly. United Nations (1992) projections are that some time in the first half of the twenty-first century, nine of the ten largest cities in the world will be in what world-system theorists would classify as semiperipheral countries.

With urbanization comes the concentration of humanly created waste, which is produced much faster than the time it takes to biodegrade. Hence, a number of environmental problems associated with urbanization will very likely continue to plague the Third World even more in the years to come. However, rural population growth also brings its unique problems. It is often the case that deforestation is precipitated by encroachment into rural areas.

An important idea in ecology is that of carrying capacity of the natural environment. Although it was originally conceptualized in terms of animal and plant species, with some important caveats, it applies to human beings as well (Catton 1980, 1994; Cohen 1995). Carrying capacity of an area refers to the number of members of a species that can live in that area. For animals, the area poses natural limits by virtue of the food and shelter available and in terms of the threats to a species’ livelihood through exposure to disease and competition from predators.

With some important caveats, many of the theories that have been developed to describe nonhuman populations can apply to human populations as well. The use of language and other complex symbol systems makes the human case quite distinct, however. Technology is made possible through those complex symbol systems and the accumulation of knowledge that accompanies them. This, in turn, makes it possible to alter the natural environment profoundly. While it is true that every species has an effect on its environment, human beings have, by far, had the most profound effect of all (Lenski 1966; Schnaiberg and Gould 1994).

Human beings can use technology to extend the carrying capacity of a place temporarily. The use of fossil fuel such as gasoline is a good example. Through techniques such as drilling into the earth and refining the crude oil found there, we are able to use energy that was fixed millennia ago. In so doing, we extend the carrying capacity, but we do so only temporarily. The oil itself takes much longer for nature to produce than for us to use it. Ecologists see the temporary extension of carrying capacity through technology as a prime case of overshoot (Catton 1980). However, it is also a principle of ecology that overshoot tends to be followed by some catastrophe that causes severe hardship and death. This condition is often referred to in the literature with the apocalyptic moniker of “crash”; historically, the greater the overshoot, the greater the severity of the eventual crash (Catton 1980; see also Diamond 2005).

As we have seen, population growth is related to environmental impact in a number of complex ways (Burns et al. 1998). Ultimately, every individual requires a certain amount of energy to survive. However, the level of affluence must be very carefully considered as well. There is a great deal of inequality, both within and among countries, in terms of the level of affluence.

In 1960, the richest 20 percent of the world’s population had an income about 30 times that of the world’s poorest 20 percent. Within one generation—by 1990—that proportion had doubled to 60—the richest fifth of the world’s population had incomes 60 times that of the poorest fifth (United Nations Development Programme 1994). With increasing affluence comes the increasing impact, or size of the “ecological footprint,” a person or a society makes (Jorgenson 2003; York, Rosa, and Dietz 2003).

Closely associated with the question of overall affluence is the question of how unevenly that affluence is distributed. In fact, one of the greatest critics of Thomas Malthus, and his ideas on overpopulation, was Karl Marx. Marx believed that the central human problem was distribution of resources, with a few people living in luxury, while many lived in poor, and increasingly desperate, conditions. While Marx had little to say about the effect of this on the environment (for an attempt to link Marx’s work with environmental concerns, see Foster 1999), the implications of his critique of Malthus are broad.

In our increasingly interconnected world, the relationship between production and environmental degradation can be seen in the context of the transnational social organization of agricultural and industrial production. This involves the control of global assembly lines, which largely involves foreign investment, and transnational corporations that are sometimes in partial cooperation with domestic firms. The process operates primarily in the interests of the firms themselves, which are largely headquartered in affluent, higher-consuming countries (Chase- Dunn 1998; Jorgenson 2003).

The findings of recent studies suggest that foreign capital penetration is a mechanism partly responsible for particular forms of environmental degradation, including carbon dioxide emissions, methane emissions, sulfur dioxide emissions, and water pollution intensity (e.g., Grimes and Kentor 2003; Shandra et al. 2004; Jorgenson 2006). It is not unusual for transnational corporations to make investments in less developed countries, which maintain lower environmental standards and policies than those found in the more affluent, high-consumption-oriented societies. A large proportion of foreign investment in less developed countries finances ecologically inefficient, labor- and energy-intensive manufacturing processes outsourced from developed countries. Moreover, power generation in the countries receiving foreign investment is considerably less efficient. This often results in increased emissions of noxious greenhouse gases (Lofdahl 2002).

Indeed, the transnational social organization of production is tied to the flows of natural resources and produced commodities between countries. Like foreign investment, international trade has become an increasingly salient issue in environmental sociology and other environmental social sciences (Lofdahl 2002; Jorgenson and Kick 2006). For example, the amount of resources a country consumes is largely a function of its level of economic development (Jorgenson 2003).

Paradoxically, nations with higher levels of resource consumption experience lower levels of environmental degradation within their borders, including deforestation and organic water pollution (Jorgenson 2003; Jorgenson and Burns 2004). International trade practices at least partially account for this paradox (e.g., Hornborg 2001; Jorgenson 2004). International trade blurs human responsibility for the environmental effects of production and consumption (e.g., Rothman 1998; Andersson and Lindroth 2001; Lofdahl 2002). Developed countries possess the international political and economic power and institutional infrastructure to achieve improvements in domestic environmental conditions while continuing to impose negative externalities (e.g., Chase-Dunn 1998; Foster 1999; Princen, Maniates, and Conca 2002).

More broadly speaking, there often is a mismatch between the logic of economics and that of ecology; while it makes sense economically to have large-scale production with many concentrations of specialized parts of the overall process around the globe, this tends to be damaging ecologically. Natural ecology works much better on a smaller scale, where waste and other by-products can be naturally recycled (Freudenburg 1990) and where production and consumption practices are more closely coupled (Foster 1999).

As we can see from the above discussion, there are numerous ways in which population processes, technology, and consumption patterns are intertwined. As a result, their influences on the environment alone and in combination are complex. Yet it is essential for social and natural scientists to continue to grapple with understanding these complexities. There is little doubt that many of the problems discussed in this research paper will get worse before they improve. Any progress that is to be made is likely to involve taking environmental problems seriously while at the same time moving the focus beyond any one single causative factor.

The specific contributory mechanisms most closely associated with environmental outcomes tend to differ by level of development of a country or region. Population processes are certainly linked with environmental outcomes, yet the level of resource consumption of a population, itself largely a function of affluence and the ways in which technologies are used, is a significant factor in environmental impact as well. Consider, for example, that per capita energy usage in the United States is over 50 times as much as in some Third World locales. Thus, although it is true that population increases have environmental consequences, it is shortsighted to stop at that observation. The ways in which populations use resources are profoundly important as well, and it is crucial to consider these factors in conjunction with one another if we are to obtain anything beyond the most simplistic of views.

That said, by virtually all projections, population will multiply significantly through at least the first half of the twenty-first century, with the most significant increases occurring in developing countries. As the human population increases, social scientists observe a number of related phenomena, such as per capita resource consumption and concentrations of population in urban areas. Higher levels of energy usage, in turn, mean greater impact on the environment, such as more extraction of fossil fuels and the degradation associated with them or more reliance on nuclear fission and, thereby, the creation of its poisonous by-products.

Increases in population and urbanization often tend to be accompanied by technological innovation, which could potentially be good for the environment (Simon 1990). Yet if history is any indicator, as new technologies are developed, they are often used to make deeper and more lasting incursions into the environment (Freudenburg and Frickel 1995). Technological innovation, thus, often has a net negative impact on the environment. As society develops in the twenty-first century, it will continue to be crucial that citizens remain vigilant about the ways in which technology is conceptualized and used.

Also of significance is the question of technological diffusion. With increasing global patterns of commerce, communication, and transportation, less developed countries are exposed to technologies heretofore typically confined to the developed world. Closely associated with technological diffusion are dramatically rising consumption patterns (e.g., Grubler 1991, 1997). Consider that with the United States currently having about 4–5 percent of the world’s population, it currently consumes about 25 percent of its energy. If every society consumed resources at the rate of developed countries, as those in North America and Western Europe do currently, the world’s resources, productive capacity, and sinks would be taxed far greater than they already are, beyond sustainable levels.

Yet consumption patterns are catching up the world over. Consider that China, the most populous country in the world, has very recently become the world’s largest consumer of a variety of commodities, from soybeans to lead and copper (Commodity Research Bureau 2005). As rapidly developing countries continue to move toward the standard of living of the most developed countries, the overall ecological impact on the planet will likely increase to heretofore unprecedented levels.

Thus, as we move well into the third millennium, we will face a number of daunting socio-environmental challenges. Air pollution and water pollution are increasingly pressing problems, which manifest themselves on a number of levels, from international to local communities. People in farming regions will increasingly have to grapple with exhaustion of topsoil in which to grow food. Worldwide, there are problems of global warming, deforestation, depletion of fresh water for drinking, and pollution of what resources there are left. Sources of food that many people have traditionally taken for granted, such as a steady supply of fish in coastal areas, are in dwindling supply.

While environmental degradation and resource depletion are worldwide problems, the specific causes and manifestations of the problems are quite distinct in different parts of the world. Certainly, the natural geography of a place— tropical, boreal, or temperate, for example—has a large effect on how people interact with the environment around them, both in terms of how they make their livelihoods and in terms of how they affect the environment. Every bit as important as the natural geography is the level of development of a country or a region—its level of affluence and technological sophistication—for this allows, and even encourages, people to have an impact on the environment.

Yet as we confront these daunting problems, a large portion of society appears to be in denial. In much of the developed world, consumption rates are at an all-time high—for example, sales of sport utility vehicles and other vehicles that consume high levels of fossil fuels and put a heavy burden on the air we breathe have increased to unprecedented levels.

There are energy technologies that are more friendly to the natural environment and thus more sustainable in the long run. However, many “alternative” fuel sources, such as solar and wind energy and hydrogen fuel cells, are not at the stage of development where they may be able to compete with fossil fuels of oil and coal in terms of costs in an open market.

Around the broad outlines we have discussed, a number of issues will continue to press society’s abilities. There will always be a need for energy sources. Inequality of access to energy and other resources will continue to be a problem. In addition to finding and making useable sources of energy and other resources, technology will need to be developed to face the inevitable consequences of making incursions into the natural ecosystem to acquire those resources.

With society moving into the twenty-first century, the challenges associated with the environment and the interrelated factors of technology, population, and patterns of consumption continue to present themselves. While societies have always faced such problems, the magnitude of environmental and technological challenges faced by the people in the twenty-first century is unprecedented in human history. There are more people than ever before with the technological wherewithal to make more profound incursions into the planet and its biosphere, consuming resources at greater rates than at any other time in human history. These factors promise to make questions regarding the environment and technology perhaps the most critical faced by society in the twenty-first century and beyond.

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Human beings cannot fly, or fight with their teeth and claws, or run, swim, or climb as handily as other animals. Instead, using our brains, we have devised tools and skills that have given us power over the natural world and permitted us to thrive almost everywhere on the planet. These tools and skills—in a word, technology—have also given some people power over others.

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The Impact of Digital Technology on Academic Performance in Low-Income School Districts

Introduction.

Over recent years, we have seen that digital technology is also quickly being adopted in education within the United States, with schools loading themselves onto various examples of technological tools to improve learning outcomes. This increase is particularly significant in low-income school districts, facilitated through the government program and an impetus to go digital, resulting in a high level of digital materials chosen for use. (Lee, 2020). Consequently, schools in impoverished areas often have a more practical goal to consider when integrating technology into their approach to teaching.

The constant demand for modernization is another vital influencing factor, mainly because technological improvements are coming at such a rapid pace. Irrespective of the socioeconomic context, all schools experience pressure to maintain a competitive edge with advancements in technological trends. This pressure includes even low-income districts, which strive to ensure students receive an education consistent with modern standards (Haleem et al., 2022). Furthermore, a general view is that the educational environment improves learning situations. There is a pervasive theory that technology can trigger positive change in education and that devices such as laptops and tablets should be deployed within the classroom to supplement study. Digital education is often passive, which might cause engagement to drop, thus providing a poor educational experience. There is also the need for help with students’ attention when using digital learning resources. Like the novelty of digital tools, enthusiasm and participation may also decrease with time, even among poor students. As such, these effects only underline the significance of detailed research to analyze the results that integration of digital technology in low-income educational settings can bring.

Societal and Economic Implications

It worsens the ital divide pro the cess that results in unequal access to educational resources and opportunities. Students from lesser privileged backgrounds and those with less reliable access to devices or high-speed Internet resources meet impediments that hinder their ability to engage in digital learning fully. The widening gap not only influences their present educational experiences but also has damaging long-term effects on future job prospects and economic possibilities. In other words, as progressive skills associated with the digital job market become much more common in our society, students who did not spend much time interacting with it while growing up might struggle to develop crucial technological competencies, thereby widening socioeconomic gaps (Kaputa et al., 2022).

Incorporating digital tools redefines the typical teacher-student relationships and classroom interactions. The trend towards technology-based learning may lead to decreased personal contact between college lecturers and students, thus potentially resulting in diminished trust relationships. It may also be that standardized approaches hinder teachers from providing tailored learning experiences. This is an essential shift in dynamics, which not only might prevent significant relationships from being established but also the educator’s ability to use teaching approaches tailored to each student’s particular needs. Since we discuss the results of technology in education, it is relevant to analyze this impact not only on academic performance but also on the general socioeconomic fabric and personal relationships within the educational environment.

Research and Expert Opinions

Researchers frequently study how widespread technology adoption can lead to distractions, reduced attention spans, and an overall drop in academic performance (Timotheou et al., 2022). The findings show the necessity of critically evaluating digital technology incorporation in low-income school districts due to possible implications for students’ educational journey.

However, outside the research community, educators and practitioners in the field also shed valuable light on some challenges arising from machines becoming human-surrogate thinkers. Many teachers are concerned about balancing traditional teaching methods and digital tools, as it may ensure that the latter does not remove some essential elements associated with face-to-face instruction. Experts often highlight the importance of teachers’ training programs to help educators acquire the appropriate skills to integrate technology effectively into their teaching practices (Singh, 2021). These insights highlight the necessity for a careful and well-considered approach to integrating technology, recognizing the specific obstacles impoverished school districts face in their efforts to provide a comprehensive education for their students.

The strengths of counterarguments for using digital education technologies list advantages such as individual learning and increasing digital literacy skills. Proponents say that technology enables personalized learning experiences based on personal student interests. Also, incorporating digital tools is viewed as a facilitator for imparting critical digital literacy skills to students to prepare them for a society where technology drives life. Although these potential advantages are admitted, it is necessary to weigh them against the problems and side effects discussed above to understand how digital technology may influence low-income school districts.

In sum, the ubiquitous presence of digital technology in impoverished school districts has a limitless number of complex impacts on academic performance, social life, and economic prospects. Government initiatives and technological breakthroughs fuel this integration, but the absence of access to required technology enhances disparities in existing educational inequality. Moreover, switching to digital learning raises the question of how this would impact interactions between teacher and student and the overall learning atmosphere. According to research findings and expert opinions, technology in education has potential benefits and challenges. Hence, there is a need to walk an even path. This overall integration is a subtle process, so policymakers, educators, and stakeholders must collaborate to confront the issues regarding access, professional development, and individualized learning to guarantee that technology’s advantages are fully conserved. At the same time, its unintended effects in poor schools are minimized.

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review.  Sustainable Operations and Computers ,  3 (3), 275–285. ScienceDirect. https://doi.org/10.1016/j.susoc.2022.05.004

Kaputa, V., Loučanová, E., & Tejerina-Gaite, F. A. (2022). Digital Transformation in Higher Education Institutions as a Driver of Social Oriented Innovations.  Innovation, Technology, and Knowledge Management , 61–85. https://doi.org/10.1007/978-3-030-84044-0_4

Lee, N. T. (2020, March 2).  Bridging digital divides between schools and communities . Brookings. https://www.brookings.edu/articles/bridging-digital-divides-between-schools-and-communities/

Singh, M. N. (2021). Inroad of Digital Technology in Education: Age of Digital Classroom.  Higher Education for the Future ,  8 (1), 20–30. https://doi.org/10.1177/2347631120980272

Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2022). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review.  Education and Information Technologies ,  28 (28). https://doi.org/10.1007/s10639-022-11431-8

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Iron as a chemical element is referred to by the symbol Fe and has an atomic number 26 on the periodic table. Iron is a metal in the first transition series and is the most abundant element on the outer and inner surface of the earth.

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