the importance of computer literacy research paper

• 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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A systematic review on digital literacy

• Hasan Tinmaz   ORCID: orcid.org/0000-0003-4310-0848 1 ,
• Yoo-Taek Lee   ORCID: orcid.org/0000-0002-1913-9059 2 ,
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• Hasnan Baber   ORCID: orcid.org/0000-0002-8951-3501 4  

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The purpose of this study is to discover the main themes and categories of the research studies regarding digital literacy. To serve this purpose, the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier were searched with four keyword-combinations and final forty-three articles were included in the dataset. The researchers applied a systematic literature review method to the dataset. The preliminary findings demonstrated that there is a growing prevalence of digital literacy articles starting from the year 2013. The dominant research methodology of the reviewed articles is qualitative. The four major themes revealed from the qualitative content analysis are: digital literacy, digital competencies, digital skills and digital thinking. Under each theme, the categories and their frequencies are analysed. Recommendations for further research and for real life implementations are generated.

Introduction

The extant literature on digital literacy, skills and competencies is rich in definitions and classifications, but there is still no consensus on the larger themes and subsumed themes categories. (Heitin, 2016 ). To exemplify, existing inventories of Internet skills suffer from ‘incompleteness and over-simplification, conceptual ambiguity’ (van Deursen et al., 2015 ), and Internet skills are only a part of digital skills. While there is already a plethora of research in this field, this research paper hereby aims to provide a general framework of digital areas and themes that can best describe digital (cap)abilities in the novel context of Industry 4.0 and the accelerated pandemic-triggered digitalisation. The areas and themes can represent the starting point for drafting a contemporary digital literacy framework.

Sousa and Rocha ( 2019 ) explained that there is a stake of digital skills for disruptive digital business, and they connect it to the latest developments, such as the Internet of Things (IoT), cloud technology, big data, artificial intelligence, and robotics. The topic is even more important given the large disparities in digital literacy across regions (Tinmaz et al., 2022 ). More precisely, digital inequalities encompass skills, along with access, usage and self-perceptions. These inequalities need to be addressed, as they are credited with a ‘potential to shape life chances in multiple ways’ (Robinson et al., 2015 ), e.g., academic performance, labour market competitiveness, health, civic and political participation. Steps have been successfully taken to address physical access gaps, but skills gaps are still looming (Van Deursen & Van Dijk, 2010a ). Moreover, digital inequalities have grown larger due to the COVID-19 pandemic, and they influenced the very state of health of the most vulnerable categories of population or their employability in a time when digital skills are required (Baber et al., 2022 ; Beaunoyer, Dupéré & Guitton, 2020 ).

The systematic review the researchers propose is a useful updated instrument of classification and inventory for digital literacy. Considering the latest developments in the economy and in line with current digitalisation needs, digitally literate population may assist policymakers in various fields, e.g., education, administration, healthcare system, and managers of companies and other concerned organisations that need to stay competitive and to employ competitive workforce. Therefore, it is indispensably vital to comprehend the big picture of digital literacy related research.

Literature review

Since the advent of Digital Literacy, scholars have been concerned with identifying and classifying the various (cap)abilities related to its operation. Using the most cited academic papers in this stream of research, several classifications of digital-related literacies, competencies, and skills emerged.

Digital literacies

Digital literacy, which is one of the challenges of integration of technology in academic courses (Blau, Shamir-Inbal & Avdiel, 2020 ), has been defined in the current literature as the competencies and skills required for navigating a fragmented and complex information ecosystem (Eshet, 2004 ). A ‘Digital Literacy Framework’ was designed by Eshet-Alkalai ( 2012 ), comprising six categories: (a) photo-visual thinking (understanding and using visual information); (b) real-time thinking (simultaneously processing a variety of stimuli); (c) information thinking (evaluating and combining information from multiple digital sources); (d) branching thinking (navigating in non-linear hyper-media environments); (e) reproduction thinking (creating outcomes using technological tools by designing new content or remixing existing digital content); (f) social-emotional thinking (understanding and applying cyberspace rules). According to Heitin ( 2016 ), digital literacy groups the following clusters: (a) finding and consuming digital content; (b) creating digital content; (c) communicating or sharing digital content. Hence, the literature describes the digital literacy in many ways by associating a set of various technical and non-technical elements.

• Digital competencies

The Digital Competence Framework for Citizens (DigComp 2.1.), the most recent framework proposed by the European Union, which is currently under review and undergoing an updating process, contains five competency areas: (a) information and data literacy, (b) communication and collaboration, (c) digital content creation, (d) safety, and (e) problem solving (Carretero, Vuorikari & Punie, 2017 ). Digital competency had previously been described in a technical fashion by Ferrari ( 2012 ) as a set comprising information skills, communication skills, content creation skills, safety skills, and problem-solving skills, which later outlined the areas of competence in DigComp 2.1, too.

• Digital skills

Ng ( 2012 ) pointed out the following three categories of digital skills: (a) technological (using technological tools); (b) cognitive (thinking critically when managing information); (c) social (communicating and socialising). A set of Internet skill was suggested by Van Deursen and Van Dijk ( 2009 , 2010b ), which contains: (a) operational skills (basic skills in using internet technology), (b) formal Internet skills (navigation and orientation skills); (c) information Internet skills (fulfilling information needs), and (d) strategic Internet skills (using the internet to reach goals). In 2014, the same authors added communication and content creation skills to the initial framework (van Dijk & van Deursen). Similarly, Helsper and Eynon ( 2013 ) put forward a set of four digital skills: technical, social, critical, and creative skills. Furthermore, van Deursen et al. ( 2015 ) built a set of items and factors to measure Internet skills: operational, information navigation, social, creative, mobile. More recent literature (vaan Laar et al., 2017 ) divides digital skills into seven core categories: technical, information management, communication, collaboration, creativity, critical thinking, and problem solving.

It is worth mentioning that the various methodologies used to classify digital literacy are overlapping or non-exhaustive, which confirms the conceptual ambiguity mentioned by van Deursen et al. ( 2015 ).

• Digital thinking

Thinking skills (along with digital skills) have been acknowledged to be a significant element of digital literacy in the educational process context (Ferrari, 2012 ). In fact, critical thinking, creativity, and innovation are at the very core of DigComp. Information and Communication Technology as a support for thinking is a learning objective in any school curriculum. In the same vein, analytical thinking and interdisciplinary thinking, which help solve problems, are yet other concerns of educators in the Industry 4.0 (Ozkan-Ozen & Kazancoglu, 2021 ).

However, we have recently witnessed a shift of focus from learning how to use information and communication technologies to using it while staying safe in the cyber-environment and being aware of alternative facts. Digital thinking would encompass identifying fake news, misinformation, and echo chambers (Sulzer, 2018 ). Not least important, concern about cybersecurity has grown especially in times of political, social or economic turmoil, such as the elections or the Covid-19 crisis (Sulzer, 2018 ; Puig, Blanco-Anaya & Perez-Maceira, 2021 ).

Ultimately, this systematic review paper focuses on the following major research questions as follows:

Research question 1: What is the yearly distribution of digital literacy related papers?

Research question 2: What are the research methods for digital literacy related papers?

Research question 3: What are the main themes in digital literacy related papers?

Research question 4: What are the concentrated categories (under revealed main themes) in digital literacy related papers?

This study employed the systematic review method where the authors scrutinized the existing literature around the major research question of digital literacy. As Uman ( 2011 ) pointed, in systematic literature review, the findings of the earlier research are examined for the identification of consistent and repetitive themes. The systematic review method differs from literature review with its well managed and highly organized qualitative scrutiny processes where researchers tend to cover less materials from fewer number of databases to write their literature review (Kowalczyk & Truluck, 2013 ; Robinson & Lowe, 2015 ).

Data collection

To address major research objectives, the following five important databases are selected due to their digital literacy focused research dominance: 1. WoS/Clarivate Analytics, 2. Proquest Central; 3. Emerald Management Journals; 4. Jstor Business College Collections; 5. Scopus/Elsevier.

The search was made in the second half of June 2021, in abstract and key words written in English language. We only kept research articles and book chapters (herein referred to as papers). Our purpose was to identify a set of digital literacy areas, or an inventory of such areas and topics. To serve that purpose, systematic review was utilized with the following synonym key words for the search: ‘digital literacy’, ‘digital skills’, ‘digital competence’ and ‘digital fluency’, to find the mainstream literature dealing with the topic. These key words were unfolded as a result of the consultation with the subject matter experts (two board members from Korean Digital Literacy Association and two professors from technology studies department). Below are the four key word combinations used in the search: “Digital literacy AND systematic review”, “Digital skills AND systematic review”, “Digital competence AND systematic review”, and “Digital fluency AND systematic review”.

A sequential systematic search was made in the five databases mentioned above. Thus, from one database to another, duplicate papers were manually excluded in a cascade manner to extract only unique results and to make the research smoother to conduct. At this stage, we kept 47 papers. Further exclusion criteria were applied. Thus, only full-text items written in English were selected, and in doing so, three papers were excluded (no full text available), and one other paper was excluded because it was not written in English, but in Spanish. Therefore, we investigated a total number of 43 papers, as shown in Table 1 . “ Appendix A ” shows the list of these papers with full references.

Data analysis

The 43 papers selected after the application of the inclusion and exclusion criteria, respectively, were reviewed the materials independently by two researchers who were from two different countries. The researchers identified all topics pertaining to digital literacy, as they appeared in the papers. Next, a third researcher independently analysed these findings by excluded duplicates A qualitative content analysis was manually performed by calculating the frequency of major themes in all papers, where the raw data was compared and contrasted (Fraenkel et al., 2012 ). All three reviewers independently list the words and how the context in which they appeared and then the three reviewers collectively decided for how it should be categorized. Lastly, it is vital to remind that literature review of this article was written after the identification of the themes appeared as a result of our qualitative analyses. Therefore, the authors decided to shape the literature review structure based on the themes.

As an answer to the first research question (the yearly distribution of digital literacy related papers), Fig.  1 demonstrates the yearly distribution of digital literacy related papers. It is seen that there is an increasing trend about the digital literacy papers.

Yearly distribution of digital literacy related papers

Research question number two (The research methods for digital literacy related papers) concentrates on what research methods are employed for these digital literacy related papers. As Fig.  2 shows, most of the papers were using the qualitative method. Not stated refers to book chapters.

Research methods used in the reviewed articles

When forty-three articles were analysed for the main themes as in research question number three (The main themes in digital literacy related papers), the overall findings were categorized around four major themes: (i) literacies, (ii) competencies, (iii) skills, and (iv) thinking. Under every major theme, the categories were listed and explained as in research question number four (The concentrated categories (under revealed main themes) in digital literacy related papers).

The authors utilized an overt categorization for the depiction of these major themes. For example, when the ‘creativity’ was labelled as a skill, the authors also categorized it under the ‘skills’ theme. Similarly, when ‘creativity’ was mentioned as a competency, the authors listed it under the ‘competencies’ theme. Therefore, it is possible to recognize the same finding under different major themes.

Major theme 1: literacies

Digital literacy being the major concern of this paper was observed to be blatantly mentioned in five papers out forty-three. One of these articles described digital literacy as the human proficiencies to live, learn and work in the current digital society. In addition to these five articles, two additional papers used the same term as ‘critical digital literacy’ by describing it as a person’s or a society’s accessibility and assessment level interaction with digital technologies to utilize and/or create information. Table 2 summarizes the major categories under ‘Literacies’ major theme.

Computer literacy, media literacy and cultural literacy were the second most common literacy (n = 5). One of the article branches computer literacy as tool (detailing with software and hardware uses) and resource (focusing on information processing capacity of a computer) literacies. Cultural literacy was emphasized as a vital element for functioning in an intercultural team on a digital project.

Disciplinary literacy (n = 4) was referring to utilizing different computer programs (n = 2) or technical gadgets (n = 2) with a specific emphasis on required cognitive, affective and psychomotor skills to be able to work in any digital context (n = 3), serving for the using (n = 2), creating and applying (n = 2) digital literacy in real life.

Data literacy, technology literacy and multiliteracy were the third frequent categories (n = 3). The ‘multiliteracy’ was referring to the innate nature of digital technologies, which have been infused into many aspects of human lives.

Last but not least, Internet literacy, mobile literacy, web literacy, new literacy, personal literacy and research literacy were discussed in forty-three article findings. Web literacy was focusing on being able to connect with people on the web (n = 2), discover the web content (especially the navigation on a hyper-textual platform), and learn web related skills through practical web experiences. Personal literacy was highlighting digital identity management. Research literacy was not only concentrating on conducting scientific research ability but also finding available scholarship online.

Twenty-four other categories are unfolded from the results sections of forty-three articles. Table 3 presents the list of these other literacies where the authors sorted the categories in an ascending alphabetical order without any other sorting criterion. Primarily, search, tagging, filtering and attention literacies were mainly underlining their roles in information processing. Furthermore, social-structural literacy was indicated as the recognition of the social circumstances and generation of information. Another information-related literacy was pointed as publishing literacy, which is the ability to disseminate information via different digital channels.

While above listed personal literacy was referring to digital identity management, network literacy was explained as someone’s social networking ability to manage the digital relationship with other people. Additionally, participatory literacy was defined as the necessary abilities to join an online team working on online content production.

Emerging technology literacy was stipulated as an essential ability to recognize and appreciate the most recent and innovative technologies in along with smart choices related to these technologies. Additionally, the critical literacy was added as an ability to make smart judgements on the cost benefit analysis of these recent technologies.

Last of all, basic, intermediate, and advanced digital assessment literacies were specified for educational institutions that are planning to integrate various digital tools to conduct instructional assessments in their bodies.

Major theme 2: competencies

The second major theme was revealed as competencies. The authors directly categorized the findings that are specified with the word of competency. Table 4 summarizes the entire category set for the competencies major theme.

The most common category was the ‘digital competence’ (n = 14) where one of the articles points to that category as ‘generic digital competence’ referring to someone’s creativity for multimedia development (video editing was emphasized). Under this broad category, the following sub-categories were associated:

Problem solving (n = 10)

Safety (n = 7)

Information processing (n = 5)

Content creation (n = 5)

Communication (n = 2)

Digital rights (n = 1)

Digital emotional intelligence (n = 1)

Digital teamwork (n = 1)

Big data utilization (n = 1)

Artificial Intelligence utilization (n = 1)

Virtual leadership (n = 1)

Self-disruption (in along with the pace of digitalization) (n = 1)

Like ‘digital competency’, five additional articles especially coined the term as ‘digital competence as a life skill’. Deeper analysis demonstrated the following points: social competences (n = 4), communication in mother tongue (n = 3) and foreign language (n = 2), entrepreneurship (n = 3), civic competence (n = 2), fundamental science (n = 1), technology (n = 1) and mathematics (n = 1) competences, learning to learn (n = 1) and self-initiative (n = 1).

Moreover, competencies were linked to workplace digital competencies in three articles and highlighted as significant for employability (n = 3) and ‘economic engagement’ (n = 3). Digital competencies were also detailed for leisure (n = 2) and communication (n = 2). Furthermore, two articles pointed digital competencies as an inter-cultural competency and one as a cross-cultural competency. Lastly, the ‘digital nativity’ (n = 1) was clarified as someone’s innate competency of being able to feel contented and satisfied with digital technologies.

Major theme 3: skills

The third major observed theme was ‘skills’, which was dominantly gathered around information literacy skills (n = 19) and information and communication technologies skills (n = 18). Table 5 demonstrates the categories with more than one occurrence.

Table 6 summarizes the sub-categories of the two most frequent categories of ‘skills’ major theme. The information literacy skills noticeably concentrate on the steps of information processing; evaluation (n = 6), utilization (n = 4), finding (n = 3), locating (n = 2) information. Moreover, the importance of trial/error process, being a lifelong learner, feeling a need for information and so forth were evidently listed under this sub-category. On the other hand, ICT skills were grouped around cognitive and affective domains. For instance, while technical skills in general and use of social media, coding, multimedia, chat or emailing in specific were reported in cognitive domain, attitude, intention, and belief towards ICT were mentioned as the elements of affective domain.

Communication skills (n = 9) were multi-dimensional for different societies, cultures, and globalized contexts, requiring linguistic skills. Collaboration skills (n = 9) are also recurrently cited with an explicit emphasis for virtual platforms.

‘Ethics for digital environment’ encapsulated ethical use of information (n = 4) and different technologies (n = 2), knowing digital laws (n = 2) and responsibilities (n = 2) in along with digital rights and obligations (n = 1), having digital awareness (n = 1), following digital etiquettes (n = 1), treating other people with respect (n = 1) including no cyber-bullying (n = 1) and no stealing or damaging other people (n = 1).

‘Digital fluency’ involved digital access (n = 2) by using different software and hardware (n = 2) in online platforms (n = 1) or communication tools (n = 1) or within programming environments (n = 1). Digital fluency also underlined following recent technological advancements (n = 1) and knowledge (n = 1) including digital health and wellness (n = 1) dimension.

‘Social intelligence’ related to understanding digital culture (n = 1), the concept of digital exclusion (n = 1) and digital divide (n = 3). ‘Research skills’ were detailed with searching academic information (n = 3) on databases such as Web of Science and Scopus (n = 2) and their citation, summarization, and quotation (n = 2).

‘Digital teaching’ was described as a skill (n = 2) in Table 4 whereas it was also labelled as a competence (n = 1) as shown in Table 3 . Similarly, while learning to learn (n = 1) was coined under competencies in Table 3 , digital learning (n = 2, Table 4 ) and life-long learning (n = 1, Table 5 ) were stated as learning related skills. Moreover, learning was used with the following three terms: learning readiness (n = 1), self-paced learning (n = 1) and learning flexibility (n = 1).

Table 7 shows other categories listed below the ‘skills’ major theme. The list covers not only the software such as GIS, text mining, mapping, or bibliometric analysis programs but also the conceptual skills such as the fourth industrial revolution and information management.

Major theme 4: thinking

The last identified major theme was the different types of ‘thinking’. As Table 8 shows, ‘critical thinking’ was the most frequent thinking category (n = 4). Except computational thinking, the other categories were not detailed.

Computational thinking (n = 3) was associated with the general logic of how a computer works and sub-categorized into the following steps; construction of the problem (n = 3), abstraction (n = 1), disintegration of the problem (n = 2), data collection, (n = 2), data analysis (n = 2), algorithmic design (n = 2), parallelization & iteration (n = 1), automation (n = 1), generalization (n = 1), and evaluation (n = 2).

A transversal analysis of digital literacy categories reveals the following fields of digital literacy application:

Technological advancement (IT, ICT, Industry 4.0, IoT, text mining, GIS, bibliometric analysis, mapping data, technology, AI, big data)

Networking (Internet, web, connectivity, network, safety)

Information (media, news, communication)

Creative-cultural industries (culture, publishing, film, TV, leisure, content creation)

Academia (research, documentation, library)

Citizenship (participation, society, social intelligence, awareness, politics, rights, legal use, ethics)

Education (life skills, problem solving, teaching, learning, education, lifelong learning)

Professional life (work, teamwork, collaboration, economy, commerce, leadership, decision making)

Personal level (critical thinking, evaluation, analytical thinking, innovative thinking)

This systematic review on digital literacy concentrated on forty-three articles from the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier. The initial results revealed that there is an increasing trend on digital literacy focused academic papers. Research work in digital literacy is critical in a context of disruptive digital business, and more recently, the pandemic-triggered accelerated digitalisation (Beaunoyer, Dupéré & Guitton, 2020 ; Sousa & Rocha 2019 ). Moreover, most of these papers were employing qualitative research methods. The raw data of these articles were analysed qualitatively using systematic literature review to reveal major themes and categories. Four major themes that appeared are: digital literacy, digital competencies, digital skills and thinking.

Whereas the mainstream literature describes digital literacy as a set of photo-visual, real-time, information, branching, reproduction and social-emotional thinking (Eshet-Alkalai, 2012 ) or as a set of precise specific operations, i.e., finding, consuming, creating, communicating and sharing digital content (Heitin, 2016 ), this study reveals that digital literacy revolves around and is in connection with the concepts of computer literacy, media literacy, cultural literacy or disciplinary literacy. In other words, the present systematic review indicates that digital literacy is far broader than specific tasks, englobing the entire sphere of computer operation and media use in a cultural context.

The digital competence yardstick, DigComp (Carretero, Vuorikari & Punie, 2017 ) suggests that the main digital competencies cover information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. Similarly, the findings of this research place digital competencies in relation to problem solving, safety, information processing, content creation and communication. Therefore, the findings of the systematic literature review are, to a large extent, in line with the existing framework used in the European Union.

The investigation of the main keywords associated with digital skills has revealed that information literacy, ICT, communication, collaboration, digital content creation, research and decision-making skill are the most representative. In a structured way, the existing literature groups these skills in technological, cognitive, and social (Ng, 2012 ) or, more extensively, into operational, formal, information Internet, strategic, communication and content creation (van Dijk & van Deursen, 2014 ). In time, the literature has become richer in frameworks, and prolific authors have improved their results. As such, more recent research (vaan Laar et al., 2017 ) use the following categories: technical, information management, communication, collaboration, creativity, critical thinking, and problem solving.

Whereas digital thinking was observed to be mostly related with critical thinking and computational thinking, DigComp connects it with critical thinking, creativity, and innovation, on the one hand, and researchers highlight fake news, misinformation, cybersecurity, and echo chambers as exponents of digital thinking, on the other hand (Sulzer, 2018 ; Puig, Blanco-Anaya & Perez-Maceira, 2021 ).

This systematic review research study looks ahead to offer an initial step and guideline for the development of a more contemporary digital literacy framework including digital literacy major themes and factors. The researchers provide the following recommendations for both researchers and practitioners.

Recommendations for prospective research

By considering the major qualitative research trend, it seems apparent that more quantitative research-oriented studies are needed. Although it requires more effort and time, mixed method studies will help understand digital literacy holistically.

As digital literacy is an umbrella term for many different technologies, specific case studies need be designed, such as digital literacy for artificial intelligence or digital literacy for drones’ usage.

Digital literacy affects different areas of human lives, such as education, business, health, governance, and so forth. Therefore, different case studies could be carried out for each of these unique dimensions of our lives. For instance, it is worth investigating the role of digital literacy on lifelong learning in particular, and on education in general, as well as the digital upskilling effects on the labour market flexibility.

Further experimental studies on digital literacy are necessary to realize how certain variables (for instance, age, gender, socioeconomic status, cognitive abilities, etc.) affect this concept overtly or covertly. Moreover, the digital divide issue needs to be analysed through the lens of its main determinants.

New bibliometric analysis method can be implemented on digital literacy documents to reveal more information on how these works are related or centred on what major topic. This visual approach will assist to realize the big picture within the digital literacy framework.

Recommendations for practitioners

The digital literacy stakeholders, policymakers in education and managers in private organizations, need to be aware that there are many dimensions and variables regarding the implementation of digital literacy. In that case, stakeholders must comprehend their beneficiaries or the participants more deeply to increase the effect of digital literacy related activities. For example, critical thinking and problem-solving skills and abilities are mentioned to affect digital literacy. Hence, stakeholders have to initially understand whether the participants have enough entry level critical thinking and problem solving.

Development of digital literacy for different groups of people requires more energy, since each group might require a different set of skills, abilities, or competencies. Hence, different subject matter experts, such as technologists, instructional designers, content experts, should join the team.

It is indispensably vital to develop different digital frameworks for different technologies (basic or advanced) or different contexts (different levels of schooling or various industries).

These frameworks should be updated regularly as digital fields are evolving rapidly. Every year, committees should gather around to understand new technological trends and decide whether they should address the changes into their frameworks.

Understanding digital literacy in a thorough manner can enable decision makers to correctly implement and apply policies addressing the digital divide that is reflected onto various aspects of life, e.g., health, employment, education, especially in turbulent times such as the COVID-19 pandemic is.

Lastly, it is also essential to state the study limitations. This study is limited to the analysis of a certain number of papers, obtained from using the selected keywords and databases. Therefore, an extension can be made by adding other keywords and searching other databases.

Availability of data and materials

The authors present the articles used for the study in “ Appendix A ”.

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Tinmaz, H., Lee, YT., Fanea-Ivanovici, M. et al. A systematic review on digital literacy. Smart Learn. Environ. 9 , 21 (2022). https://doi.org/10.1186/s40561-022-00204-y

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An assessment of the interplay between literacy and digital Technology in Higher Education

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Digital technologies fundamentally transform teaching and learning in higher education environments, with the pace of technological change exacerbating the challenge. Due to the current pandemic situation, higher education environments are all now forced to move away from traditional teaching and learning structures that are simply no longer adaptable to the challenges of rapidly changing educational environments. This research develops a conceptual model and employs Structural Equation Modelling (SEM) using Partial least Squares (PLS) to examine the impact of information and digital literacy on 249 Finnish university staff and students’ intention to use digital technologies. The findings show the complex interrelationship between literacy skills and digital technologies among university staff and students. The results illustrate that information literacy has a direct and significant impact on intention to use; while, unlike our expectation, digital literacy does not have a direct impact on the intention to use. However, its effect is mediated through performance expectancy and effort expectancy. The authors suggest that to understand the changes that are taking place in higher education environment, more attention needs to be paid to redefining policies and strategies in order to enhance individuals’ willingness to use digital technologies within higher education environments.

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1 Introduction

Higher education environments, among others, are still struggling to come to terms with digitalisation and trying to find optimal ways to maximise the benefits such as higher student engagement levels, connecting traditional classroom to a virtual landscape and the enhancement of the student learning process (Gupta et al. 2020 ; Rabah 2015 ). The recent global crisis, and in particular the current pandemic situation, has required a major reform in higher education (Skulmowski and Rey 2020 ). As such, in response to these challenges, higher education institutions are now looking into accelerating the process of digitalisation and the transition from on-campus teaching to technology-enhanced learning—with an increasing focus on the move towards online and distance learning. To respond to this emerging issue, transition in many higher education institutions has already taken place although it was rushed and unplanned and are currently looking at fine-tuning processes, assessing and making improvements.

Digital technologies have become an integral part of daily activities in modern education, and the use of such technologies (e.g. computer-mediated communication (CMC) tools, subject-specific learning tools, interactive whiteboards, desktop or mobile videoconferencing, mobile applications/computer software, gaming consoles, tablets and smartphones (Desjardins and van Oostveen 2015 ; Major et al. 2018 ; van Oostveen and Desjardins 2013 ), is now even expected within formal learning environments (Lodge et al. 2019 ). Moreover, among other digital educational tools, learning management systems (LMSs) such as Moodle and Blackboard, while already in use (e.g. Carvalho et al. 2011 ) have recently gained popularity as essential tools within the field of education (Rachmadtullah et al. 2020 ). LMSs help to create, deploy, and maintain fully digital forms of education and provide a meaningful e-learning experience for users of such systems. Learning management systems (LMSs) provide many benefits and advantages to teachers (e.g. by facilitating the tracking of learner progress and performance) and to students (e.g. by integrating social learning experiences into learning strategy) (Sarrab et al. 2013 ; Zheng et al. 2018 ) the effective and efficient use of such technologies depends on prerequisite skills such as digital literacy and information literacy (Avcı and Ergün 2019 ; Tang and Chaw 2016 ).

Moreover, other authors such as (Turnbull et al. 2019 ) stated that learning management systems (LMSs) not only provide content to learners and enable to (i) disseminate knowledge, (ii) assess the learner competency, (iii) record the learner attainment, but also facilitate timely and accurate communication between learners and teachers and provide support for online social communities. All in all, we argue that in the higher education context, the development of a set of competencies that enables efficient and effective use of digital technologies is critical requisite for students’ success and lifelong learning.

While, in our information-based society, literacy skills have become survival skills for economic development and community well-being (Desjardins 2001 ). Within higher education literacy is considered an essential enabler for problem solving, critical thinking, lifelong learning, and to function adequately for both veteran educators (Pastore and Andrade 2019 ; Záhorec et al. 2019 ), and new generations of students (Gullikson 2006 ; Leahy and Dolan 2010 ). Blau and Shamir-Inbal ( 2017 , p. 781) found that the use of ICT in their lessons and enhancing their pedagogical skills, their digital competence all impacts the general ICT integration into education. Furthermore, as distance education has become a trend in modern teaching and learning, research on intention to use digital technology once again has attracted the attention of several scholars from different disciplines (e.g. Lazar et al. 2020 ; Liu et al. 2020 ). As such, not only is it important to investigate the impact of digital and information literacy skills on education but concurrently to pay equal attention to individuals’ intention to use digital technology. In this paper, digital literacy refers to “the awareness, attitude and ability of individuals to appropriately use digital tools and facilities to identify, access, manage, integrate, evaluate, analyse and synthesise digital resources” (Martin 2006 , p. 155). Information literacy refers to “the understanding and set of abilities enabling individuals to recognise when information is needed and have the capacity to locate, evaluate and use effectively the needed information” (American Library Association (ALA) 2000 ). However, few studies have been conducted on these aspects. Within higher education, these two aspects— (i) literacy skills and (ii) intention to use digital technologies for teaching and learning purposes (e.g. Anthonysamy et al. 2020 ; Jeffrey et al. 2011 ; Nikou et al. 2018 )—have been extensively investigated, but seldom together.

In addition, from a theoretical standpoint, different models have been used to assess intention to use digital technology within higher education. For instance, Almaiah et al. ( 2019 ), employed an integrated model of the unified theory of acceptance and use of technology (UTAUT), to assess university students’ intention to use handheld devices for learning (i.e. mobile learning or “m-learning”). To investigate literacy skills among teachers, Georgina and Hosford ( 2009 , p. 695), examined university staff’s perception towards digital skills and found that empowering a university-wide professoriate to enhance both their information literacy and technology literacy skills is the new goal in higher education. Regarding students’ literacy skills, recent studies have indicated that literacy skills play a vital role in students’ education and performance (e.g. Foo et al. 2014 ; Kanniainen et al. 2019 ). For example, Kanniainen et al. ( 2019 ), found that literacy skills impact students’ online research and comprehension (ORC) performance.

To this end, the main objectives of this research were twofold: (1) based on the existing theoretical models (e.g. Ng 2012 ; Venkatesh et al. 2003 ), it aimed to develop an integrated conceptual model to assess the factors that influence Finnish university staff and students’ intention to use digital technologies, and (2) through an empirical research, it aimed to create new information and knowledge concerning the interplay between literacy skills and intention to use digital technology. Primarily, this research was conducted to answer the following question: “To what extent do the literacy skills of university staff and students influence their intentions to use technology, and how does effort expectancy and performance expectancy mediate these relationships”?

To assess the above-mentioned question, a series of hypotheses were proposed, and data was collected via an online questionnaire completed by university staff (e.g. professors and researchers) and students. The data was analysed through structural equation modelling (SEM) using SmartPLS v.3.

The remainder of this paper is structured as follows: section two provides a literature review; section three, based on the review results, describes a theory-based conceptual model and puts forward several hypotheses. Section four describes the research methodology followed by research results. Section six provides the discussion and section seven offers a final conclusion.

2 Literacy competences

While there is no commonly accepted definition of literacy or what it means to be literate (Keefe and Copeland 2011 ), the concept of literacy is traditionally thought of as the ability to read and write. Literacy skills are fostered in the educational system; they are intertwined with our everyday activities and can be utilised in measuring a population’s level of education. However, alongside the development of our contemporary society, often referred to a knowledge-based economy, the concept of literacy has evolved and gone past its traditional definition. For example, the concept of twenty-first century digital skills, which includes abilities such as collaboration, communication, digital literacy, citizenship, problem solving, critical thinking, interpersonal capabilities, creativity and productivity has extensively been discussed in the literature (Dede 2010 ; Van Laar et al. 2017 ). Lewin and McNicol ( 2015 ) argued that twenty-first century skills are essential individual competencies in our contemporary digital workplace. Moreover, Shields and Chugh ( 2018 ) critically reviewed the concept of twenty-first century skills and argued that there is a strong rationale for the teaching of twenty-first century skills to students. The authors claimed that it cannot be assumed that because learners in the twenty-first century use Information and Communications Technology (ICT) in their education, they know how to use the technology effectively and efficiently for performing different activities (p. 1105).

In the context of twenty-first century skills, forms of literacy such as information literacy (e.g. Eisenberg 2008 ; Lloyd 2006 ), and digital literacy (e.g. Eshet-Alkalai 2004 ; Ng 2012 ), are now regarded as highly important in terms of achieving success in higher education. In this paper, while we acknowledge the importance of twenty-first century skills in the higher education context, we specifically focus on information and digital literacy as the development of such abilities not only is critical requisite for students’ success in online learning environments, but also the acquisition of these skills is one of the necessary tools for facilitating lifelong learning (Ala-Mutka 2011 ). Information and digital literacy skills provide an array of abilities—for example, to (i) access and search for information efficiently and effectively using a variety of digital tools, (ii) critically evaluate the reliability of sources for an academic context, (iii) filter, manage, and organise information from a wide variety of sources, and (iv) understand how to use digital tools for referencing to avoid plagiarism. Abilities in relation to evaluation and effective use of information have always been a central component of learning but recently have reached a new level of significance in the modern era as they have morphed into a “survival skill” (Eshet-Alkalai 2004 ).

As defined by Eshet-Alkalai ( 2004 ), information literacy encompasses “the cognitive skills that consumers use to evaluate information in an educated and effective manner” (p. 101). An alternative definition by the American Library Association (American Library Association (ALA) 2000 ), on the other hand, states that information literacy encompasses abilities related to recognising when information is needed and then being able to locate, evaluate, and use information effectively. Information literacy has received scholarly attention regarding all levels of formal education, as studies have been conducted on topics ranging from early childhood education to tertiary education (e.g. Dunn 2002 ; Gross and Latham 2012 ; Heider 2009 ; von Loh and Henkel 2014 ). Digital literacy, on the other hand, encompasses “the multiplicity of skills associated with the use of digital technologies” (Ng 2012 , p. 1066). The utilisation of digital technologies may involve the employment of hardware and software for varying tasks within a multitude of activities, an evident example being for teaching and learning purposes. Colwell et al. ( 2013 ), stated that digital literacy skills are increasingly important in developing informed citizens who can critically evaluate information and ideas. Other authors such as Jacobs ( 2006 ), argued that digital literacy skills could have implications for how younger generations are being prepared for participation in today’s society. The concept of digital literacy has; however, caused ambiguity owing to the wide usage within differing contexts (Eshet-Alkalai 2004 ). To combat this notion, scholars have proposed frameworks aimed at clarifying the process. For example, Ng ( 2012 ), considers the concept of digital literacy as the intersection of a technical dimension, a cognitive dimension, and a socio-emotional dimension. In addition to information literacy, digital literacy has also gained its share of scholarly attention in relation to the educational setting (e.g. Lea and Jones 2011 ; Meyers et al. 2013 ).

To date, the concept of literacy has been explored as an area of research within the context of a technology acceptance and in association with UTAUT model. Mohammadyari and Singh ( 2015 ), have looked at digital literacy and the impact of UTAUT constructs on intention to continue using IT as the proximal predictor of performance. The authors hypothesised that digital literacy impacts performance through performance expectancy and effort expectancy. The constructs of performance expectancy and effort expectancy originate from the unified theory of acceptance and use of technology (UTAUT) model developed by Venkatesh et al. ( 2003 ). The UTAUT model was originally developed within an organisational context and a consumer context in order to investigate use behaviour. Mohammadyari and Singh ( 2015 ), altered some of the UTAUT constructs, resulting in a model that consisted of performance expectancy, effort expectancy, social influence, continuance intention to use IT, and performance, in addition to digital literacy. The findings indicated that digital literacy has a positive effect on performance expectancy and effort expectancy, whereas performance expectancy affects continuance intention to use IT, which in turn affects overall performance (Mohammadyari and Singh 2015 ). However, further research and development on the topic are needed and that digital literacy requires an expanded research focus from the perspective of information technology usage as an evolving skill (Mohammadyari and Singh 2015 ).

3 Theoretical background

The present research extends UTAUT by including digital literacy and information literacy as key predictors of the intention to use digital technologies for teaching and learning purposes. Digital literacy and information literacy are particularly relevant in the context of higher education because they allow us to conceptualise the use of the educational digital technologies as an evolving skill. Despite the accolades given to UTAUT and UTAUT2 constructs for their predictive ability, and based on the context of the study, constructs such as hedonic motivation, habit, and social influence have been deliberately excluded from the model. For instance, use behaviour, which is the outcome variable in the UTAUT models (Venkatesh et al. 2003 ; Venkatesh et al. 2012 ), was not selected for the present study because we did not investigate the actual usage, but rather the intention to use. Hedonic motivation was excluded owing to the fact that university staff and students are required to utilise digital technologies in their work tasks; thus, assessing the pleasure derived from the use of technology was less relevant in the context of the research at hand. The same reasoning was applied to habit, as the usage of certain digital technologies is dictated by circumstances. The constructs of social influence and facilitating conditions were excluded, as they are external variables and thus not considered to occur within the realms of information literacy and digital literacy. Finally, the construct of price value was excluded owing to this research being carried out at some Finnish universities, meaning that staff and students had free access to technologies on campus.

Figure 1 shows our research model. We adapted UTAUT by including digital literacy (DL) and information literacy (IL) as additional predictors of university staff and students’ intention to use digital technologies (INT) in addition to the two core constructs of UTAUT, performance expectancy (PE) and effort expectancy (EE). The following sections detail our reasons for these alterations in the model and explicate the hypotheses in the model.

Conceptual model

3.1 Information literacy

According to the American Library Association (ALA) ( 2000 ), information literacy is defined as abilities related to recognising when information is needed and then being able to locate, evaluate, and use information effectively. As noted by Johnston and Webber ( 2003 ), IL emphasis on recognising an information need, evaluating what is found, and using the information effectively. The relationship between IL and intention to use digital technology has been investigated by, for example, Alateyah et al. ( 2013 ) and Kong et al. ( 2019 ). Furthermore, previous studies by Nikou et al. ( 2018 , 2019 ), have indicated a significant relationship between IL and attitude towards the use of digital technologies, and attitude in turn was found to have a significant effect on intention to use. Within the context of the UTAUT framework, the influence of its constructs on IL has received some scholarly attention (e.G. Ali and Gupta 2019 ).

In the present paper, we propose that a higher level of IL has a direct effect on intention to use digital technology, but also positively affects the productivity of university students and staff. In other words, a higher level of IL will influence staff and students’ performance and effort expectations in relation to using digital technology for teaching and learning purposes. Thus, these relationships may be represented by the following hypotheses:

H1a: Information literacy has a positive effect on intention to use digital technology

H1b: Information literacy has a positive effect on performance expectancy

H1c: Information literacy has a positive effect on effort expectancy

3.2 Digital literacy

In this paper, digital literacy (DL) is characterised as a mixture of technical, cognitive, and socio-emotional factors associated with the utilisation of digital technologies (Ng 2012 ). A relationship between DL and intention to use has been investigated in the research of, for example, Bayrakdaroğlu and Bayrakdaroğlu ( 2017 ). Moreover, previous research by Nikou et al. ( 2018 ) has indicated that a significant relationship exists between certain components of DL (i.e. technical and social-emotional factors) and attitude towards the use of digital technologies, and in turn towards intention to use digital technologies. In the context of the UTAUT2 model, prior research by Mohammadyari and Singh ( 2015 ), has indicated that DL is positively related to performance expectancy and effort expectancy. Therefore, we propose that a higher level of DL not only has a direct effect on intention to use digital technology but also positively influences the productivity of university staff and students. In other words, DL has a direct positive impact on staff and students’ performance and effort expectations in relation to using digital technology for teaching and learning purposes. Thus, these relationships may be represented by the following hypotheses:

H2a: Digital literacy has a positive effect on intention to use digital technologies

H2b: Digital literacy has a positive effect on performance expectancy

H3c: Digital literacy has a positive effect on effort expectancy

3.3 Performance expectancy

Performance expectancy (PE) refers to the extent to which using digital technologies benefits university staff and students in performing their teaching and learning activities. The definition used in this research is an adaption of the PE construct used in the UTAUT model (Venkatesh et al. 2003 ), whereby the construct has been used within an organisational context and a consumer context. PE has received a varied level of scholarly attention within the educational context (e.g. Abu-Al-Aish and Love 2013 ; Chen 2011 ; Diep et al. 2016 ; Nassuora 2012 ). In terms of the effect of PE on intention, previous research has indicated that PE can indeed be a significant predictor of intention (e.g. Ghalandari 2012 ).

We propose that an individual with high levels of IL and DL skills may expect to put in less effort while using digital technology for teaching and learning activities, and simultaneously expect to exhibit a better performance. The hypothesis concerning this construct would indicate the existence of performance benefits achieved by its use (Chávez Herting et al. 2020 ). Consequently, this relationship may be represented by the following hypothesis:

H3: Performance expectancy has a positive effect on intention to use digital technology

3.4 Effort expectancy

Effort expectancy (EE) refers to the extent of ease associated with university staff and students’ use of digital technology. The definition is likewise modified from its original context in the UTAUT model (Venkatesh et al. 2003 ). The EE has also received its share of scholarly attention within the educational context (e.g. Abu-Al-Aish and Love 2013 ; Chen 2011 ; Nassuora 2012 ). In terms of the effect of EE on intention to use technology, prior research (Ghalandari 2012 ; Lowenthal 2010 ), suggests that EE is a significant predictor of intention. In university contexts, the adoption of digital technologies may involve less time directed towards academic responsibilities such as teaching (research) and learning (Liu et al. 2020 ). In other words, if the use of technology requires a greater effort than non-use, one can speculate that use decreases accordingly. We theorise that an individual with high levels of IL and DL skills may expect to put in less effort while using digital technology for teaching and learning activities, and simultaneously expect to have a higher degree of ease associated with their use of technology. Therefore, this relationship may be represented by the following hypothesis:

H4: Effort expectancy has a positive effect on intention to use digital technology

3.5 Intention to use digital technology

Intention to use as an outcome variable has been used in seminal studies on technology use (Ajzen 1985 ; Davis 1989 ; Fishbein and Ajzen 1975 ; Venkatesh et al. 2012 ). In the research at hand, intention to use technology (INT) refers to university staff and students’ intention to use digital technologies for teaching and learning purposes. In this research, we aimed to investigate how intention to use digital technology can be affected by information literacy and digital literacy while concurrently accounting for performance expectancy and effort expectancy. Moreover, we also assessed whether PE and EE mediate the relationships between DL and IL and intention to use.

4 Research methodology

The proposed conceptual model is assessed through partial least squares structural equation modelling (PLS-SEM). This approach is appropriate for exploratory research when seeking to develop theories (Hair et al. 2017 ). According to Hair et al. ( 2019 ) PLS-SEM facilitates the opportunity to “estimate complex models with many constructs, indicator variables and structural paths without imposing distributional assumptions on the data” (p. 2).

4.1 Measures

To examine the path relationships among five constructs in the model, an online survey was designed. Items for measuring performance expectancy, effort expectancy and intention to use were borrowed from Venkatesh et al. ( 2003 ). Digital literacy and information literacy were measured using items from Ng ( 2012 ) and Ahmad et al. ( 2020 ) and Kurbanoglu et al. ( 2006 ). In total, 34 items and their contents were adapted for the use of digital technologies for teaching and learning purposes.

4.2 Data collection

To attain the opinions and experiences of the research target group—university staff and students—data was collected in June 2019 over the course of five weeks. The questionnaire consisted of three sections: questions concerning the respondent background, conceptual model construct items, and an open-text field where respondents could provide any additional insights regarding their experiences. The background information questions addressed respondent gender, age, access to digital technologies, frequency of use of digital technology, and self-reported proficiency level with digital tools. In terms of the items used to measure the construct in the model, respondents were advised to answer questions on a seven-point Likert scale ranging from one (“Strongly disagree”) to seven (“Strongly agree”). Pilot testing and the respondent debriefing were conducted with a small group of participants (university lecturers, professors, and students) before distributing the questionnaire to assess reliability and comprehension (Hess and Singer 1995 ). The questionnaire was then distributed through various channels, including survey invitations through university staff and students’ emails and university-related groups on social media sites. Additionally, flyers were distributed at four campuses of three Finnish universities in Finland.

5 Data analysis

Over 500 invitations were sent to potential respondents and we received 257 responses. After the removal of unengaged respondents and incomplete responses, 249 responses were eligible for further analysis. In terms of the respondents’ current position within the university, the majority—153 (61.4%)—reported to be students, 90 (36.1%) were teaching and research staff, eight (3.2%) were administration staff, and 29 (11.6%) employees had other duties at the university. The question assessing occupation within the university consisted of a multiple-choice matrix, meaning that respondents who identified both as staff and as administrative personnel did not have to choose between the two roles.

5.1 Descriptive analysis

Of 249 respondents, 147 (59.0%) females and 99 (39.8%) males, the remaining three (1.2%) identifying as “other”, 153 were students and 96 were university staff and others. The majority of the respondents (58.6%) were aged 20–29 years. In terms of the highest level of education 87 (34.9%) were reported to hold a bachelor’s degree, 57 (22.9%) a PhD, 53 (21.3%) a master’s degree, and 49 (19.7%) a high school diploma. Table 1 shows the descriptive results regarding the access to digital devices. Respondents were asked to rate their access (use) to smartphone, tablet, desktop PC, laptop and wearable devices in their day-to-day activities, ranging from “I do not use” to “Several times each day.” For example, almost all respondents reported that they use their smartphones several times each day. The highest average use of digital devices was smartphones (M = 4.94) and laptops (M = 4.31), while the least used digital devices were wearable devices (M = 1.68) and tablets (M = 2.05). When we compared the results between university staff and students, some interesting results emerged. For instance, it was clear that as much as two-thirds of the university staff used tablets, whereas 90 students indicated that they did not use tablets at all. Among students, 49 indicated that they do not use desktop PCs, whereas only 16 university staff reported that they do not use PCs anymore.

Moreover, as shown in Table 2 , we asked respondents to report the frequency of use of digital technologies (software and applications) in their day-to-day activities, ranging from “I do not use” to “Several times each day.” The results showed that the most frequently used application was reported to be email (M = 4.77), followed by social media (M = 4.57). When we compared staff and students’ frequency of use of software (application), 129 students indicated that they use social media and 113 indicated that they use email applications several times each day. The results for the university staff showed that they all use email applications on daily basis and 62 reported that they use social media applications several times each day.

Table 3 shows the descriptive results regarding the respondents’ proficiency with digital technologies (software or applications). The respondents were asked to report their level of proficiency with (word processor, spreadsheet, PPT slides, file sharing, photo editing, website management tools, mobile calendar, email applications and social media platforms) on a scale ranging from one (“Not proficient at all”) to seven (“Very proficient”). The results show that the majority of the respondents were confident that they had a high level of proficiency with such digital technologies (software and applications). For example, the average proficiency rate regarding the use of word processors was (M = 5.91), followed the use of social media (M = 5.52). However, the highest proficiency rate concerned the use of email (M = 6.11). The lowest proficiency rate concerned the use of website management tools (M = 2.56), which was to be expected owing to the context of this research and the respondents’ positions at the time of the study. When we compared staff and students’ proficiency levels regarding the use of digital technologies, 63 students indicated that they were very proficient in the use of email applications, but only 53 indicated that they were very proficient in the use of social media platforms. This finding was unexpected, as we assumed that students of recent generations spend much of their time using different social media platforms. Regarding the university staff’s proficiency with digital technologies, seven indicated that they were not proficient with social media at all, while 40 indicated that they were very proficient.

5.2 Measurement model results

To assess the research model, we assessed both the measurement model and structural model. The measurement model’s reliability and validity were determined through the examination of outer loadings, composite reliability (CR) and convergent validity as presented by average variance extracted (AVE). According to Hair et al. ( 2011 ), the values of the outer loadings, CR, and AVE should be above 0.70, 0.70, and 0.50, respectively. The values obtained for part of the data analysis comply with recommendations. However, a few items had low factor loadings and were eliminated from further analysis. In addition, all the Cronbach’s alpha values were above the threshold of 0.70 (see Table 4 for more information).

5.3 Discriminant validity

The discriminant validity was examined according to the Fornell and Larcker ( 1981 ) criterion. As shown in Table 5 , the discriminant validity was confirmed.

We additionally assessed the discriminant validity via the heterotrait-monotrait ratio of correlations (HTMT). This alternative to the classical criterion can be applied to measure discriminant validity by comparing formerly outlined threshold levels, such as 0.85 (Kline 2011 ) or 0.90 (Teo et al. 2008 ). As shown in Table 6 , the results show that the discriminant validity was confirmed.

5.4 Structural model results

To test the hypotheses and assess the statistical significance of the path coefficients in the research model, we used a structural equation modelling approach. PLS-SEM relies on the SRMR (standardized root mean square residual) value for model testing, of which the conservative recommended threshold is 0.08. In our data, the value for the model fit is 0.07. The intention to use digital technology is explained by a variance of 40%. Both constructs of the UTAUT model—performance expectancy and effort expectancy —can be explained by variances of 27% and 67%, respectively.

5.5 Hypothesis testing

Different alternative models were tested, and the model presented in Fig.  2 is the optimal model and best fits the data. The SEM results revealed a direct significant relationship between information literacy and intention to use digital technology (β = 0.21, t  = 3.071, p  < 0.001); thus, H1a was supported by the model. Additionally, the SEM results revealed no direct significant relationships between information literacy and effort expectancy as well as information literacy and effort expectancy. Thus, both H2b and H2c were rejected by the model.

Structural model results. Note: * p value <0.01; ** p value <0.005; *** p value <0.001

Furthermore, the SEM results revealed that the path relationship between digital literacy and intention to use digital technology was not significant, thus rejecting H2a in the model. Moreover, the SEM results revealed a direct significant relationship between digital literacy and performance expectancy (β = 0.48, t  = 6.678, p  < 0.001), thereby supporting H2b in the model. The SEM results revealed that the relationship between digital literacy and effort expectancy was also significant (β = 0.85, t  = 18.191, p  < 0.001); thus, H2c is supported by the model. Moreover, the SEM results revealed a direct significant relationship between performance expectancy and intention to use digital technology (β = 0.47, t  = 8.591, p  < 0.001), thus providing theoretical support for H3 in the model. Finally, the SEM results revealed a direct significant relationship between effort expectancy and intention to use digital technology (β = 0.18, t  = 2.024, p  < 0.001); thus, H4 is supported by the model.

We also performed multi-group analysis to examine if the path relationships were different between students and staff. The MGA did not show any significant difference between the two groups except for one path. Such that the relationship between effort expectancy to intention to use digital technology was significant only for the students (β = 0.28, t  = 1.991, p  < 0.05).

5.6 Mediation effect

To examine the mediation effects of effort expectancy and performance expectancy on the paths between DL and IL to intention to use technology, we performed a mediation test. The results showed that both EE and PE mediate the relationship between DL and intention to use technology. This indicates that although we could not establish a direct relationship in this path, the entire effect of DL to intention to use was fully mediated through these two constructs. The paths between DL-EE-INT (β = 0.17, t  = 2.116, p  < 0.05) and the path between DL- PE-INT (β = 0.17, t  = 4.434, p  < 0.001) were found to be significant. Moreover, the mediation test results showed that the path between IL and intention to use was only mediated through PE (β = 0.08, t  = 2.090, p  < 0.05) and that EE did not mediate this path relationship.

6 Discussion

The use of digital technology for teaching and learning purposes seems to have become a common practice in university context. The conceptual model presented in this paper arises from (i) the need to highlight the importance of literacy skills in the context of technology use among university staff and students, and (ii) the call by Mohammadyari and Singh ( 2015 ), for further research on digital literacy. As a response to the said need and call, the research model sought to assess the intention to use digital technologies through a novel approach of combining technology adoption constructs with information literacy and digital literacy constructs. It has been argued that the research on literacy skills is a highly contemporary topic within higher education and a vital component in students’ education and performance (e.g. Foo et al. 2014 ; Kanniainen et al. 2019 ).

In this paper, we proposed that a higher level of IL would have a direct effect on intention to use digital technology, as well as a positive impact on the productivity of university students and staff. In other words, we stated that a higher level of IL would influence the staff and students’ performance and effort expectations in relation to using digital technology for teaching and learning purposes. Our findings confirmed two of the three hypotheses, as positive relationships were identified between IL and intention to use, as well as IL and performance expectancy. The relationship between IL and intention to use expands upon the findings of Nikou et al. ( 2018 , 2019 ) where a relationship was found between IL and attitude towards the use of digital technologies, as well as attitude and intention to use. To the best of the authors’ knowledge, there is a lack of studies to date confirming a relationship between IL and performance expectancy hence, there is a lack of alignment between the findings of our research and prior literature.

As with DL, we suggested that a higher level of DL would not only have a direct effect on intention to use digital technology, but also a positive impact on the productivity of university staff and students. In other words, we stated that DL would have a direct positive impact on staff and students’ performance and effort expectations regarding the use of digital technology for teaching and learning purposes. The results of our research confirmed two of the three relationships, as we identified positive relationships between DL and performance expectancy, as well as DL and effort expectancy. These findings align with the findings of previous studies, e.g. Mohammadyari and Singh ( 2015 ).

Furthermore, we proposed that an individual with high levels of IL and DL skills might expect to put in less effort while using digital technologies for teaching and learning activities, and simultaneously expect to have a better performance. Subsequently, guided by the relationship within the UTAUT framework (Venkatesh et al. 2003 ), we hypothesised that performance expectancy would have a positive relationship with intention to use digital technology. Our findings confirmed this hypothesis, thus aligning the result with those of Ghalandari ( 2012 ) and Chávez Herting et al. ( 2020 ). Additionally, we theorised that an individual with high levels of IL and DL skills may expect to put in less effort while using digital technology for teaching and learning activities, and simultaneously expect to have a higher degree of ease associated with their use of technology. Subsequently, guided by the relationship within the UTAUT framework (Venkatesh et al. 2003 ), we hypothesised that effort expectancy would have a positive relationship with intention to use digital technology. The findings of our research confirmed this hypothesis, which aligns with the results of previous studies conducted by Ghalandari ( 2012 ) and Lowenthal ( 2010 ).

Moreover, the descriptive data analysis revealed some interesting aspects of the sample. In terms of the sample as a whole, when asked about access to digital technologies, the highest scoring variables when accounting for the mean were smartphones and laptops. In terms of the frequency of software application use, Microsoft Word processors and email services scored the highest. This was also the case with proficiency of software application use, as email services and Microsoft word processing services received the highest mean values. Clear differences were also identified between university staff and students, such as the fact that the use of tablets and desktop computers is more prominent among staff. In addition, another interesting finding was the fact that within the student sample, the number of respondents who considered themselves very proficient with email services was higher than the number of students who considered themselves very proficient with social media applications.

7 Conclusion

By applying performance expectancy and effort expectancy from the UTAUT framework (Venkatesh et al. 2003 ) accompanied by information literacy and digital literacy, this research builds upon prior literature through the development of a new conceptual model that aimed to explore university staff and students’ intention to use digital technologies. The assessment of the developed model was then conducted using PLS-SEM. The research conducted in this paper could be considered a response to Silber-Varod et al. ( 2019 ), who stated that despite the importance of digital skills for effective learning in educational settings, research literature in this domain is marginal. The results of the current research provide support to earlier findings such as (Aavakare and Nikou 2020 ; Lea and Jones 2011 ), indicating that there is a complex interrelationship between literacies and technologies or (Nikou et al. 2020 ; Rosman et al. 2018 ), who indicated that information literacy skills do not necessarily imply that students employ the newly acquired skills for their own searches. Our findings are also supported by earlier results of (Mercader 2020 , p. 5144), who stated that training and the level of digital skills are two factors that should be considered in the incorporation of digital technologies in higher education. The results of this research have several important implications both for curriculum design and for higher education policy. Firstly, the results show that both expectations regarding effort and performance have positive impacts on intention to use digital technology. These findings align with those of prior literature (Ghalandari 2012 ; Lowenthal 2010 ), and in the research at hand, the results reveal that university staff and students’ intention to use digital technologies for teaching and learning purposes is impacted by their perceived expectations regarding performance and effort.

Secondly, the results indicate that a direct significant relationship exists between information literacy and the intention to use digital technology. This finding is aligned with those of prior findings such as (Rosman et al. 2018 , p. 97), showing that the literacy skills are important for self-regulated learning since they allow individuals to construct their knowledge. Meanwhile, no such relationship could be identified between digital literacy and intention to use, despite our initial expectation. The lack of a significant relationship between digital literacy and intention to use could possibly be explained through the context of the research as well as the nature of the respondents participating in this research project. The sample consisted mainly of university students (millennials often known as digital natives) and university staff. These groups of people are assumed to be digitally literate individuals as they use, and have access to, various digital tools and applications in their daily teaching and learning tasks. Therefore, for them the use of digital technologies in their learning and teaching is a natural behaviour. The SEM, however, indicate that the impact of digital literacy on intention to use is mediated via performance expectancy and effort expectancy, thus establishing partial mediations. Some authors such as (Colwell et al. 2013 ), argue that digital literacy skills and capabilities are important for the students, as it seems locating and evaluating information found on the Internet are most challenging for them and teachers should have realistic expectations.

Thirdly, the findings of our research highlight specifically the importance of information literacy in relation to university staff and students’ intention to use digital technologies. This is consistent with prior research of Colwell et al. ( 2013 ), who indicated that information literacy is important, as many students are reluctant to critically evaluate information found at multiple sites. Thus, there exists a need to emphasise the development of university staff and students’ literacy skills in order to cope with the struggle within higher education environments mentioned by Gupta et al. ( 2020 )—that is, to find the optimal ways to truly reap the benefits of educational digital technologies. Going beyond the promotion of digital tools within education, policy makers need to account and advocate for the literacy skills which ultimately will help with the acceptance of digital technologies within higher education institutes, as the effective and efficient use of these technologies depends on prerequisite skills such as digital literacy and information literacy (Avcı and Ergün 2019 ; Tang and Chaw 2016 ). Concludingly, strategies should be developed to encourage the progression of the dimensions of literacy that will facilitate the leap towards future educational technologies and expectations of future educational environments.

Fourthly, for educational environments, selection of the relevant digital technology and harmonising it with the overall teaching and learning strategy is crucial for universities in order to reap the full benefits of emerging technologies. In addition, it is pivotal to understand the factors that influence university staff and students’ intention to use digital technologies in their teaching and learning activities. The results of our research indicated that those higher education environments that nurture their employees and students and focus on holistic development also indirectly improve their core activities: teaching and learning.

7.1 Limitations and future work

As the data for this research was gathered via a self-completion survey, it is necessary to consider the limitation brought up by the inability to oversee respondent quality. Owing to the closed-ended survey questions, respondent answers were naturally limited. Further research could assess this limitation by incorporating qualitative approaches and expand the research in ways that allow for more open-ended answers (semi-structured interviews). In addition, further research on the topic should be conducted within other university settings, as the research at hand comprised of four campuses of three Finnish universities; therefore, generalising the findings is difficult. Thus, the knowledge on the subject matter could be expanded culturally and even comparatively between different types of university settings. Finally, the presented conceptual model could be elaborated upon through the inclusion of other constructs, such as computer self-efficacy.

Data availability

Available per request.

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Nikou, S., Aavakare, M. An assessment of the interplay between literacy and digital Technology in Higher Education. Educ Inf Technol 26 , 3893–3915 (2021). https://doi.org/10.1007/s10639-021-10451-0

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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Quantum Physics

Title: qiskit-torch-module: fast prototyping of quantum neural networks.

Abstract: Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks, especially for training variational quantum algorithms. Focusing on the widely used Qiskit software environment, we develop the qiskit-torch-module. It improves runtime performance by two orders of magnitude over comparable libraries, while facilitating low-overhead integration with existing codebases. Moreover, the framework provides advanced tools for integrating quantum neural networks with PyTorch. The pipeline is tailored for single-machine compute systems, which constitute a widely employed setup in day-to-day research efforts.

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