hypothesis of online learning

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Chapter 5 Theories and Frameworks for Online Education

Seeking an integrated model.

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In this chapter you will examine theoretical frameworks and models that focus on the pedagogical aspects of online education. After a review of learning theory as applied to online education, a proposal for an integrated Multimodal Model for Online Education is provided based on pedagogical purpose. The model attempts to integrate the work of several other major theorists and model builders such as Anderson (2011).

1 Introduction

In a provocative chapter of The Theory and Practice of Online Learning , Terry Anderson (2011) examines whether a common theory for online education can be developed. While recognizing that as a difficult, and perhaps fruitless, task, he nonetheless examines possibilities and proposes his own theory which he admits is not complete. The purpose of this article is to examine theoretical frameworks relevant to the pedagogical aspects of online education. It starts with a consideration of learning theories and funnels down to their specific application to online education. The article concludes with a proposal for an integrated model for online education based on pedagogical purpose.

2 Learning Theory

Learning theory is meant to explain and help us understand how people learn; however, the literature is complex and extensive enough to fill entire sections of a library. It involves multiple disciplines, including psychology, sociology, neuroscience, and of course, education. Three of the more popular learning theories – behaviorism, cognitivism, and social constructivism – will be highlighted to form the foundation for further discussion. Mention will also be made of several other learning theories that are relevant to online education. Before reviewing these theories, it will be worthwhile to have a brief discussion of the term theory itself.

Theory is defined as a set of statements, principles, or ideas that relate to a particular subject. A theory usually describes, explains, and/or predicts phenomena. The definition of theory also varies depending upon disciplines, especially when related to the term model. As noted by Graham, Henrie, and Gibbons (2013), the two terms are used interchangeably and generally refer to the same concept. However, a model is more frequently a visual representation of reality or a concept. In this discussion, the terms theory and model will be used interchangeably. The purpose of a theory or model is to propose the answers to basic questions associated with a phenomenon. Graham, Henrie and Gibbons (2013) reviewed this issue as related to instructional technology and recommended a three-part taxonomy first proposed by Gibbons and Bunderson (2005) that includes theories that:

• – Explore: “What exists?” and attempts to define [describe] and categorize;
• – Explain: “Why does this happen?” and looks for causality and correlation, and work with variables and relationships.
• – Design : “How do I achieve this outcome?” and describes interventions for reaching targeted outcomes and operational principles (Graham, Henrie, & Gibbons, 2013, p. 13).

This taxonomy will serve as an overall guiding principle for the discussion of learning theories and models in this article.

3 Behaviorism

As its name implies, behaviorism focuses on how people behave. It evolved from a positivist worldview related to cause and effect. In simple terms, action produces reaction. In education, behaviorism examines how students behave while learning. More specifically, behaviorism focuses on observing how students respond to certain stimuli that, when repeated, can be evaluated, quantified, and eventually controlled for each individual. The emphasis in behaviorism is on that which is observable and not on the mind or cognitive processes. In sum, if you cannot observe it, it cannot be studied.

The development of behaviorism is frequently associated with Ivan Pavlov, famous for his experiments with dogs, food, and audible stimuli, such as a bell. In his experiments, dogs learned to associate food or feeding time with the sound of the bell and began to salivate. Pavlov conducted his experiments in the early 1900s and they were replicated by many other researchers throughout the 20th century. John B. Watson, among the first Americans to follow Pavlov’s work, saw it as a branch of natural science. Watson became a major proponent of Pavlov and is generally credited with coining the term behaviorism. He argued that mind and consciousness are unimportant in the learning process and that everything can be studied in terms of stimulus and response.

Other major figures associated with behaviorism are B.F. Skinner and Edward Thorndike. Skinner is particularly well known, primarily because he introduced what he referred to as operant conditioning which emphasized the use of both positive and negative reinforcement to help individuals learn new behaviors. This was quite different from Pavlov, who relied on simple reflexive responses to specific stimuli although both Pavlov and Skinner promoted repetitive behavior that leads to habit formation. Skinner had a significant influence on early computer-assisted instructional ( CAI ) models as developed by Pat Suppes and others. A common aspect of early CAI programs was the reliance on encouragement and repetition to promote positive learning activities.

4 Cognitivism

Cognitivism has been considered a reaction to the “rigid” emphasis by behaviorists on predictive stimulus and response (Harasim, 2012, p. 58). Cognitive theorists promoted the concept that the mind has an important role in learning and sought to focus on what happens in between the occurrence of environmental stimulus and student response. They saw the cognitive processes of the mind, such as motivation and imagination, as critical elements of learning that bridge environmental stimuli and student responses. For example, Noam Chomsky (1959) wrote a critical review of Skinner’s behaviorist work in which he raised the importance of creative mental processes that are not observable in the physical world. Although written mainly from the perspective of a linguist, Chomsky’s view gained popularity in other fields, including psychology. Interdisciplinary in nature, cognitive science draws from psychology, biology, neuroscience, computer science, and philosophy to explain the workings of the brain as well as levels of cognitive development that form the foundation of learning and knowledge acquisition. As a result, cognitivism has evolved into one of the dominant learning theories. The future of cognitivism is particularly interesting as more advanced online software evolves into adaptive and personalized learning applications that seek to integrate artificial intelligence and learning analytics into instruction.

Behaviorism led to the development of taxonomies of learning because it emphasized the study and evaluation of multiple steps in the learning process. Behaviorists repeatedly studied learning activities to deconstruct and define the elements of learning. Benjamin Bloom (1956) was among the early psychologists to establish a taxonomy of learning that related to the development of intellectual skills and to stress the importance of problem solving as a higher order skill. Bloom’s (1956) Taxonomy of educational objectives handbook: Cognitive domains remains a foundational text and essential reading within the educational community. Bloom’s taxonomy is based on six key elements (see Figure 5.1 ) as follows:

• – Creating: Putting elements together to form a coherent or functional whole, and reorganizing elements into a new pattern or structure through generating, planning, or producing.
• – Evaluating: Making judgments based on criteria and standards through checking and critiquing.
• – Analyzing: Breaking material into constituent parts, and determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing.
• – Applying: Carrying out or using a procedure through executing or implementing.
• – Understanding: Constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining.
• – Remembering: Retrieving, recognizing, and recalling relevant knowledge from long-term memory.

Bloom’s taxonomy

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Bloom, in developing his taxonomy, essentially helped to move learning theory toward issues of cognition and developmental psychology. Twenty years later, Robert Gagne, an educational psychologist, developed another taxonomy (events of instruction) that built on Bloom’s and became the basis for cognitivist instructional design (Harasim, 2012). Gagne emphasized nine events in instruction that drive the definitions of objectives and strategies for the design of instructional material (see Figure 5.2 ).

Gagné’s nine events of instruction

5 social constructivism.

Parallel to behaviorism and cognitivism was the work of several education theorists, including Lev Vygotsky, John Dewey, and Jean Piaget. Their focus on social constructionism was to describe and explain teaching and learning as complex interactive social phenomena between teachers and students. Vygotsky posited that learning is problem solving and that the social construction of solutions to problems is the basis of the learning process. Vygotsky described the learning process as the establishment of a “zone of proximal development” in which the teacher, the learner, and a problem to be solved exist. The teacher provides a social environment in which the learner can assemble or construct with others the knowledge necessary to solve the problem. Likewise, John Dewey saw learning as a series of practical social experiences in which learners learn by doing, collaborating, and reflecting with others. While developed in the early part of the 20th century, Dewey’s work is very much in evidence in a good deal of present-day social constructivist instructional design. The use of reflective practice by both learner and teacher is a pedagogical cornerstone for interactive discussions that replaces straight lecturing, whether in a face-to-face or online class. Jean Piaget, whose background was in psychology and biology, based his learning theory on four stages of cognitive development that begin at birth and continue through one’s teen years and beyond. Seymour Papert, in designing the Logo programming language, drew from Jean Piaget the concept of creating social, interactive microworlds or communities where children, under the guidance of a teacher, solve problems while examining social issues, mathematical and science equations, or case studies. Papert’s approach of integrating computer technology into problem solving is easily applied to many facets of instructional design.

6 Derivatives of the Major Learning Theories

A number of theories and models have roots in one or more of the above frameworks. In the latter part of the 20th century, the major learning theories, especially cognitive theory and social constructivism, began to overlap. For example, Wenger and Lave (1991) and Wenger (1998) promoted concepts such as “communities of practice” and situated learning. Their position was that learning involves a deepening process situated in, and derived from, participation in a learning community of practice. Their work is very evident in many studies, including those related to online education.

Information processing learning theory is a variation of cognitivism that views the human mind as a system that processes information according to a set of logical rules. In it, the mind is frequently compared to a computer that follows a set of rules or program. Research using this perspective attempts to describe and explain changes in the mental processes and strategies that lead to greater cognitive competence as children develop. Richard Atkinson and Richard Shiffrin (1968) are generally credited with proposing the first information processing model that deals with how students acquire, encode, store (in short-term or long-term memory), and retrieve information.

One of the more popular and controversial theories relates to learning styles and posits that individuals learn differently depending upon their propensities and personalities. Carl Jung argued that individual personality types influence various elements of human behavior, including learning. Jung’s theory focuses on four basic psychological dimensions:

• – Extroversion vs. Introversion
• – Sensation vs. Intuition
• – Thinking vs. Feeling
• – Judging vs. Perceiving

While each unique dimension can influence an individual learning style, it is likely that learning styles are based on a combination of these dimensions. For example, a learning style might include elements of extroversion, sensation, feeling, and perception as personality dimensions. Readers may be familiar with the Myers-Briggs Type Inventory ( MBTI ) which has been used for decades to assist in determining personality types, including how personality relates to student learning. The MBTI is based extensively on Jung’s theories and has been used to predict and develop different teaching methods and environments and to predict individual patterns of mental functioning, such as information processing, idea development, and judgment formation. It can also be used to foretell patterns of attitudes and interests that influence an individual’s preferred learning environment and to predict a person’s disposition to pursue certain learning circumstances and avoid others. Lin, Cranton, and Bridglall (2005) remind us that much of the work of Carl Jung and the MBTI is applicable to learning environments, whether face-to-face or online. For example, the extrovert may prefer active, highly collaborative environments while the introvert would prefer less interaction and less collaboration. This suggests that instruction should be designed to allow both types of individuals – the outgoing social organizer as well as the introspective reflective observer – to thrive.

Howard Gardner has developed a theory of “multiple intelligences” that proposes that intelligence is not merely a singular entity but consists of multiple entities used by individuals in different proportions to understand and to learn about the world. Gardner has identified nine basic intelligences: linguistic, logical/mathematical, spatial, musical, bodily kinesthetic, interpersonal, intrapersonal, naturalistic, and existential (see Figure 5.3 ).

Gardner’s multiples intelligences (Source: Gardner, 1983)

Gardner’s theory has received criticism from both psychologists and educators who view these “intelligences” as talents, personality traits, and abilities. His work has also been questioned by those who propose that there is, in fact, a root or base intelligence that drives the other “intelligences.” Gardner does not necessarily disagree with this latter position but maintains that other intelligences can be viewed as main branches off the base root intelligence. This theory has important pedagogical implications and suggests the design of multiple learning modalities that allow learners to engage in ways they prefer, according to their interest or ability, and to challenge them to learn in other ways that are less related to their preferences, interests, or abilities. Gardner’s work also addresses the common concern that too much teaching and learning is linguistically based (reading, writing, and speaking) and that the other intelligences are underutilized.

Modern neuroscience research also suggests that students learn in different ways depending upon a number of factors including age, learning stimuli, and the pace of instruction. Willingham (2008) suggests that learning is a dynamic process that may evolve and change from one classroom to another, from one subject to another, and from one day to another. This research also supports the concept that multiple intelligences and mental abilities do not exist as mere “yes/no” entities but within continua which the mind blends in a manner consistent with the way it responds and learns from the external environment and instructional stimuli. Conceptually, this suggests a framework for a multimodal instructional design that relies on a variety of pedagogical techniques, delivery approaches, and media.

Lastly, Malcom Knowles (1998) deserves mention as the individual who distinguished between andragogy (adult learning) and pedagogy (child learning). Adults, whether seeking to enhance their professional skills or to satisfy curiosity about a subject, learn differently than children. Courses designed for adults should tap into their social contexts and experiences. Knowles’ insights are especially important for higher education, where online technology is used extensively for adult students in traditional and continuing education programs, competency-based learning, and career/professional development.

In sum, a number of theories have been, and will continue to be, applied to instruction, including online and blended learning. Several theories specifically related to online education will now be examined.

7 Learning Theories for Online Education

Just as no single learning theory has emerged for instruction in general, the same is true for online education. A number of theories have evolved, most of which derive from the major learning theories discussed previously. In this section, several theories will be examined in terms of their appropriateness for the online environment.

7.1 Community of Inquiry (CoI)

The “community of inquiry” model for online learning environments developed by Garrison, Anderson, and Archer (2000) is based on the concept of three distinct “presences”: cognitive, social, and teaching (see Figure 5.4 ). While recognizing the overlap and relationship among the three components, Anderson, Rourke, Garrison, and Archer (2001) advise further research on each component. Their model supports the design of online and blended courses as active learning environments or communities dependent on instructors and students sharing ideas, information, and opinions. Of particular note is that “presence” is a social phenomenon and manifests itself through interactions among students and instructors. The community of inquiry has become one of the more popular models for online and blended courses that are designed to be highly interactive among students and faculty using discussion boards, blogs, wikis, and videoconferencing.

Community of inquiry (from Garrison, Anderson, Garrison, & Archer, 2000).

7.2 connectivism.

George Siemens (2004), one of the early MOOC pioneers, has been the main proponent of connectivism, a learning model that acknowledges major shifts in the way knowledge and information flows, grows, and changes because of vast data communications networks. Internet technology has moved learning from internal, individualistic activities to group, community, and even crowd activities. In developing the theory, Siemens acknowledged the work of Alberto Barabasi and the power of networks. He also referenced an article written by Karen Stephensen (1998) entitled “What Knowledge Tears Apart, Networks Make Whole,” which accurately identified how large-scale networks become indispensable in helping people and organizations manage data and information.

Siemens describes connectivism as:

the integration of principles explored by chaos, network, and complexity and self-organization theories [where] learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more and are more important than our current state of knowing. (Siemens, 2004)

Siemens noted that connectivism as a theory is driven by the dynamic of information flow. Students need to understand, and be provided with, experiences in navigating and recognizing oceans of constantly shifting and evolving information. Siemens proposed eight principles of connectivism (see Figure 5.5 ). Connectivism is particularly appropriate for courses with very high enrollments and where the learning goal or objective is to develop and create knowledge rather than to disseminate it.

Siemens’ eight principles of connectivism

7.3 online collaborative learning ( ocl ).

Online collaborative learning ( OCL ) is a theory proposed by Linda Harasim that focuses on the facilities of the Internet to provide learning environments that foster collaboration and knowledge building. Harasim (2012) describes OCL as:

a new theory of learning that focuses on collaborative learning, knowledge building, and Internet use as a means to reshape formal, non-formal, and informal education for the Knowledge Age. (p. 81)

Like Siemens, Harasim sees the benefits of moving teaching and learning to the Internet and large-scale networked education. In some respects, Harasim utilizes Alberto Barabasi’s position on the power of networks. In OCL , there exist three phases of knowledge construction through discourse in a group:

Idea generating: the brainstorming phase, where divergent thoughts are gathered

Idea organizing: the phase where ideas are compared, analyzed, and categorized through discussion and argument

Intellectual convergence: the phase where intellectual synthesis and consensus occurs, including agreeing to disagree, usually through an assignment, essay, or other joint piece of work (Harasim, 2012, p. 82).

OCL also derives from social constructivism, since students are encouraged to collaboratively solve problems through discourse and where the teacher plays the role of facilitator as well as learning community member. This is a major aspect of OCL but also of other constructivist theories where the teacher is not necessarily separate and apart but rather, an active facilitator of, knowledge building. Because of the importance of the role of the teacher, OCL is not easy to scale up. Unlike connectivism, which is suited for large-scale instruction, OCL is best situated in smaller instructional environments. This last issue becomes increasingly important when seeking commonality among online education theories.

Many other theories can be associated with online education but, rather than present more theories and in keeping with one of the major purposes of this article, it is appropriate to ask whether an integrated or unified theory of online education is possible.

8 Can We Build a Common Integrated Theory of Online Education?

As noted, Terry Anderson (2011) examined the possibility of building a theory of online education, starting with the assumption that it would be a difficult, and perhaps impossible, task. He approached this undertaking from a distance education perspective, having spent much of his career at Athabasca University, the major higher education distance education provider in Canada. While he acknowledged that many theorists and practitioners consider online learning as “a subset of learning in general” (Anderson, 2011, pp. 46–47), he also stated:

online learning as a subset of distance education has always been concerned with provision of access to educational experience that is, at least more flexible in time and in space as campus-based education. (Anderson, 2011, p. 53)

These two perspectives (subset of learning in general and subset of distance education) complicate any attempt to build a common theory of online education. Blended learning models, for instance, do not easily fit into the distance education schema, even though they are evolving as a prevalent component of traditional face-to-face and online education environments.

Anderson considered a number of theories and models but focused on the well-respected work of Bransford, Brown, and Cocking (1999) who posited that effective learning environments are framed within the convergence of four overlapping lenses: community-centeredness, knowledge-centeredness, learner-centeredness, and assessment centeredness. These lenses provided the foundational framework for Anderson’s approach to building an online education theory, as he examined in detail the characteristics and facilities that the Internet provides with regards to each of the four lenses. Second, he noted that the Internet had evolved from a text-based environment to one in which all forms of media are supported and readily available. He also accurately commented that the Internet’s hyperlink capacity is most compatible with the way human knowledge is stored and accessed. In this regard, he referred to the work of Jonassen (1992) and Shank (1993) who associated hyperlinking with constructivism. Finally, Anderson extensively examined the importance of interaction in all forms of learning and referred to a number of mostly distance education theorists such as Holmberg (1989), Moore (1989), Moore and Kearsley (1996), and Garrison and Shale (1990). The essence of interaction among students, teachers, and content is well understood and is referenced in many theories of education, especially constructivism. Anderson’s evaluation of interaction concludes that interactions are critical components of a theory.

With these three elements in mind (the Bransford, Brown, and Cocking lenses, the affordances and facilities of the Internet, and interaction), Anderson then proceeded to construct a model (see Figure 5.6 ). He did add one important element by distinguishing community/collaborative models from self-paced instructional models, commenting that community/collaborative models and self-paced instructional models are inherently incompatible. The community/collaborative models do not scale up easily because of the extensive interactions among teachers and students. On the other hand, the self-paced instructional models are designed for independent learning with much less interaction among students and teachers.

Figure 5.6 illustrates

Anderson’s online learning model (reprinted with permission from Anderson, 2011)

the two major human actors, learners and teachers, and their interactions with each other and with content. Learners can of course interact directly with content that they find in multiple formats, and especially on the Web; however, many choose to have their learning sequenced, directed, and evaluated with the assistance of a teacher. This interaction can take place within a community of inquiry, using a variety of Net-based synchronous and asynchronous activities … These environments are particularly rich, and allow for the learning of social skills, the collaborative learning of content, and the development of personal relationships among participants. However, the community binds learners in time, forcing regular sessions or at least group-paced learning. The second model of learning (on the right) illustrates the structured learning tools associated with independent learning. Common tools used in this mode include computer-assisted tutorials, drills, and simulations. (Anderson, 2011, pp. 61–62)

Figure 5.6 demonstrates the instructional flow within the two sides and represents the beginnings of a theory or model from the distance education perspective. Anderson concluded that his model “will help us to deepen our understanding of this complex educational context” (Anderson, 2011, p. 68), which he noted needs to measure more fully the direction and magnitude of each input variable on relevant outcome variables.

Anderson also commented about the potential of the Internet for education delivery, and that an online learning-based theory or model could subsume all other modes with the exception of the “rich face-to-face interaction in formal classrooms” (Anderson, 2011, p. 67). This becomes a quandary for Anderson in trying to develop a common theory of online education in that it does not provide for in-person, face-to-face activity and is problematic for those who see online education as a subset of education in general.

9 An Integrated Model

Anderson’s model assumed that none of the instruction is delivered in traditional, face-to-face mode, and so excluded blended learning models that have some face-to-face component. Is it possible, therefore, to approach the search for an integrated model for online education from the face-to-face education in general or even the blended learning perspective?

Bosch (2016), in a review of instructional technology, identified and compared four blended learning models using twenty-one different design components. These models emphasized, to one degree or another, the integration of pedagogy and technology in course design. Among the models was a Blending with Pedagogical Purpose Model (see Figure 5.7 ), developed by this author, in which pedagogical objectives and activities drive the approaches, including the online technology that faculty members use in instruction. The model also suggests that blending the objectives, activities, and approaches within multiple modalities might be most effective for, and appeal to, a wide range of students. The model contains six basic pedagogical goals, and approaches for achieving them, to form learning modules. The model is flexible and assumes that other modules can be added as needed and where appropriate. The most important feature of this model is that pedagogy drives the approaches that will work best to support student learning. The modules are also shown as intersecting but this is optional; they may or may not intersect or overlap depending upon the approaches used. For instance, some reflection can be incorporated into collaboration or not, depending upon how the collaborative activity is designed. It might be beneficial to have the collaborative groups reflect specifically on their activities. Similar scenarios are possible for the other modules. Ultimately important is that all the modules used blend together into a coherent whole. The following paragraphs briefly review each of these modules.

Blending with pedagogical purpose model

Content is one of the primary drivers of instruction and there are many ways in which content can be delivered and presented. While much of what is taught is delivered linguistically (teacher speaks/students listen or teacher writes/students write), this does not have to be the case, either in face-to-face or online environments. Mayer (2009) has done extensive reviews of the research and has concluded that learning is greatly enhanced by visualization. Certain subject areas, such as science, are highly dependent upon the use of visual simulations to demonstrate processes and systems. The humanities, especially art, history, and literature, can be greatly enhanced by rich digital images as well. Course/learning management systems ( CMS / LMS ) such as Blackboard, Canvas, or Moodle provide basic content delivery mechanisms for blended learning and easily handle the delivery of a variety of media including text, video, and audio. Games have also evolved and now play a larger role in instructional content. In providing and presenting content, the Blending with Pedagogical Purpose model suggests that multiple technologies and media be utilized.

The Blending with Pedagogical Purpose model posits that instruction is not simply about learning content or a skill but also supports students socially and emotionally . As noted, constructivists view teaching and learning as inherently social activities. The physical presence of a teacher or tutor, in addition to providing instruction, is comforting and familiar. While perhaps more traditionally recognized as critical for K-12 students, social and emotional development must be acknowledged as important to education at all levels. Faculty members who have taught graduate courses know that students, even at this advanced level, frequently need someone with whom to speak, whether to help understand a complex concept or to provide advice about career and professional opportunities. While fully online courses and programs have evolved to the point where faculty members can provide some social and emotional support where possible and appropriate, in blended courses and programs this is more frequently provided in a face-to-face mode.

Dialectics or questioning is an important activity that allows faculty members to probe what students know and to help refine their knowledge. The Socratic Method remains one of the major techniques used in instruction, and many successful teachers are proud of their ability to stimulate discussion by asking the “right” questions to help students think critically about a topic or issue. In many cases, these questions serve to refine and narrow a discussion to very specific “points” or aspects of the topic at hand, and are not meant to be open-ended activities. For dialectic and questioning activities, a simple-to-use, threaded electronic discussion board or forum such as VoiceThread is an effective approach. A well-organized discussion board activity generally seeks to present a topic or issue and have students respond to questions and provide their own perspectives, while evaluating and responding to the opinions of others. The simple, direct visual of the “thread” also allows students to see how the entire discussion or lesson has evolved. In sum, for instructors who want to focus attention and dialogue on a specific topic, the main activity for many online courses has been, and continues to be, the electronic discussion board.

Reflection can be incorporated as a powerful pedagogical strategy under the right circumstances. There is an extensive body of scholarship on the “reflective teacher” and the “reflective learner” dating from the early 20th century (Dewey (1916), Schon (1983)). While reflection can be a deeply personal activity, the ability to share one’s reflections with others can be beneficial. Pedagogical activities that require students to reflect on what they learn and to share their reflections with their teachers and fellow students extend and enrich reflection. Blogs and blogging, whether as group exercises or for individual journaling activities, have evolved into appropriate tools for student reflection and other aspects of course activities.

Collaborative learning has evolved over decades. In face-to-face classes, group work grew in popularity and became commonplace in many course activities. Many professional programs, such as business administration, education, health science, and social work, rely heavily on collaborative learning as a technique for group problem solving. In the past, the logistics and time needed for effective collaboration in face-to-face classes were sometimes problematic. Now, email, mobile technology, and other forms of electronic communication alleviate some of these logistical issues. Wikis, especially, have grown in popularity and are becoming a staple in group projects and writing assignments. They are seen as important vehicles for creating knowledge and content, as well as for generating peer-review and evaluation (Fredericksen, 2015). Unlike face-to-face group work that typically ended up on the instructor’s desk when delivered in paper form, wikis allow students to generate content that can be shared with others during and beyond the end of a semester. Papers and projects developed through wikis can pass seamlessly from one group to another and from one class to another.

Evaluation of learning is perhaps the most important component of the model. CMS s/ LMS s and other online tools and platforms provide a number of mechanisms to assist in this area. Papers, tests, assignments, and portfolios are among the major methods used for student learning assessment, and are easily done electronically. Essays and term projects pass back and forth between teacher and student without the need for paper. Oral classroom presentations are giving way to YouTube videos and podcasts. The portfolio is evolving into an electronic multimedia presentation of images, video, and audio that goes far beyond the three-inch, paper-filled binder. Weekly class discussions on discussion boards or blogs provide the instructor with an electronic record that can be reviewed over and over again to examine how students have participated and progressed over time. They are also most helpful to instructors to assess their own teaching and to review what worked and what did not work in a class. Increasingly, learning analytics are seen as the mechanisms for mining this trove of data to improve learning and teaching. In sum, online technology allows for a more seamless sharing of evaluation and assessment activities, and provides a permanent, accessible record for students and teachers.

The six components of the model described above form an integrated community of learning in which rich interaction, whether online or face-to-face, can be provided and blended across all modules. Furthermore, not every course must incorporate all of the activities and approaches of the model. The pedagogical objectives of a course should drive the activities and, hence, the approaches. For example, not every course needs to require collaborative learning or dialectic questioning. In addition to individual courses, faculty and instructional designers might consider examining an entire academic program to determine which components of the model best fit with overall programmatic goals and objectives. Here, the concept of learning extends beyond the course to the larger academic program where activities might integrate across courses. For example, some MBA programs enroll a cohort of students into three courses in the same semester but require that one or more assignments or projects be common to all three courses.

The critical question for our discussion, however, is whether this Blending with Pedagogical Purpose model can be modified or enlarged to be considered a model for online education in general. By incorporating several of the components from other theories and models discussed earlier in this article, this is a possibility. Figure 5.8 presents a Multimodal Model for Online Education that expands on the Blending with Purpose approach and adds several new components from Anderson and others, namely, community, interaction, and self-paced, independent instruction.

Multimodal model for online education

First, the concept of a learning community as promoted by Garrison, Anderson, and Archer (2000) and Wenger and Lave (1991) is emphasized. A course is conceived of as a learning community. This community can be extended to a larger academic program. Second, it is understood that interaction is a basic characteristic of the community and permeates the model to the extent needed. Third, and perhaps the most important revision, is the addition of the self-study/independent learning module that Anderson emphasized as incompatible with any of the community-based models. In this model, self-study/independent learning can be integrated with other modules as needed or as the primary mode of instructional delivery. Adaptive learning software, an increasingly popular form of self-study, can stand alone or be integrated into other components of the model. The latter is commonly done at the secondary school level where adaptive software programs are used primarily in stand-alone mode with teachers available to act as tutors when needed. Adaptive software is also integrated into traditional, face-to-face classes, such as science, where it is possible to have the instructor assign a lab activity that uses adaptive learning simulation software.

This Multimodal Model of Online Education attempts to address the issues that others, particularly Terry Anderson, have raised regarding elements that might be needed for an integrated or unified theory or model for online education. Whether or not this model finds acceptance is not yet clear. It is hoped that this article might serve as a vehicle for a critical examination of the model.

10 Applying the Integrated Model

To provide a clearer understanding of the integrated model, several examples of its application follow. Figure 5.9 provides an example of the model as a representation of a self-paced, fully online course. The three major components [in green] for this course are: content as provided on an LMS / CMS , a self-paced study module, and assessment/evaluation. Other components of the model, such as a blog or discussion board to allow interaction among students, could be included but are not necessarily needed. This example is most appropriate for online programs that have rolling admissions and students are not limited by a semester schedule. Students proceed at their own pace to complete the course as is typical in some distance education programs. This example is scalable and can be used for large numbers of students.

Figure 5.10 provides an example of another course that is primarily a self-paced, online course similar to that described in Figure 5.9 but is designed to have a teacher or tutor available as needed. A discussion board is also included to allow for ongoing interaction among students and teacher. This course would follow a semester schedule and would have a standard class size although most of the instruction would be provided by the self-paced study module. A standard course organization would be used, with a teacher or tutor assigned to guide and assist with instruction. The teacher or tutor could help students struggling with any of the self-paced material. This type of course is increasingly common in secondary schools, such as in credit-recovery courses.

Example of a distance education course

Example of a modified distance education course

Figure 5.11 provides an example of a teacher-led, fully online course. Presentation of the course content is provided by a LMS or CMS along with other media and is used as needed by the teacher. The discussion board, blog, and wiki provide facilities for interaction among teachers and students, students and students, and students and content. In this course, the teacher could direct students to watch a fifteen-minute lecture available in the LMS database and then ask students to respond to a series of questions on the discussion board. Student responses can then be used as the basis for an interactive discussion board activity among students, guided by the teacher. The model also provides for reflection and collaborative activities.

Example of a teacher-led fully online course

Figure 5.12 provides an example of a blended course with instruction provided primarily by a teacher. The other modules are used to extend and enrich instruction. The teacher is the major guide for instruction and would be supplemented by content as needed by a LMS / CMS . The course would meet in a face-to-face classroom although some instructional activity would also be conducted online, either on a discussion board, a blog, or a collaborative wiki. The teacher would establish beforehand portions of the course that would meet in the face-to-face and online modes.

Example of a mainstream blended course

11 attributes and limitations of the multimodal model.

The proposed Multimodal Model for Online Education includes many of the major attributes of other learning and online education theories and models. For example, behaviorists will find elements of self-study and independent learning in adaptive software. Cognitivists might appreciate reflection and dialectic questioning as important elements of the model. Social constructivists will welcome the emphasis on community and interaction throughout the model. Connectivists might value the collaboration and the possibility of student-generated content. Perhaps the most significant element of the model is its flexibility and ability to expand as new learning approaches, perhaps spurred by advances in technology, evolve.

The model is not without limitations. Learning theories can be approached through a number of perspectives and disciplines. Behavioral psychologists, cognitive psychologists, sociologists, and teacher educators might emphasize the need for deeper considerations of their perspectives for an online learning theory. The multimodal model here represents an integrated composite of several such perspectives but is essentially a pedagogical model and, therefore, may have greater appeal to instructional designers, faculty, and others who focus on learning objectives.

12 Conclusion

In this article, a number of major theories related to technology were presented, beginning with a review of major theories associated with learning. One critical question concerned whether an integrated or unified theory of online education could be developed. The work of Terry Anderson was highlighted. The article proposed an integrated model that described the phenomenon of pedagogically driven online education. Key to this model is the assumption that online education has evolved as a subset of learning in general rather than a subset of distance learning. As blended learning, which combines face-to-face and online instruction, evolves into the dominant form of instruction throughout all levels of education, it serves as the basis for an integrated model. It is likely that, in the not-too-distant future, all courses and programs will have some online learning components, as suggested in this integrated model.

• – This chapter does not address change theory, an important, practical theory for any leader of Distance Learning. Go online to explore how to facilitate change. Identify at least three strategies that you would apply as a distance learning leader.
• – Given Siemen’s eight Principles of Connectivism and Harasim’s three phases of Online Collaborative Learning, describe an activity incorporating these ideas through which you could lead faculty members toward using the Internet constructively in their learning.
• – Using Picciano’s Multimodel Model for Online Education, analyze one or more courses that you have taken or that you have taught by identifying the components that apply to that course or courses.

Acknowledgment

This chapter was previously published and is used here with permission from the author and publisher: Picciano, A. G. (2017). Theories and frameworks for online education: Seeking an integrated model. Online Learning , 21 (3), 166–190. doi: 10.24059/olj.v21i3.1225

Anderson , T . ( 2011 ). The theory and practice of online learning (2nd ed.). AU Press.

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E-Learning Theory

E-learning theory is built on cognitive science principles that demonstrate how the use and design of educational technology can enhance effective learning (David, 2015; Wang 2012). The theory was developed from a set of principles created based on Cognitive Load Theory (Sweller, Van Merriënboer & Paas, 2019). According to David (2015), Cognitive Load Theory is “the amount of mental effort involved in working memory” (n.p.) during a task and can be categorized into germane, intrinsic, and extraneous effort. Since the working memory has limited capacity and the brain will suffer from overload if learners are presented with too much information, causing inefficient learning, it is essential to balance these three types of load to promote learning efficiency (Clark, Nguyen & Sweller, 2005). Based on this, Mayer, Sweller and Moreno (2015) established 11 design principles that were created to reduce extraneous cognitive load and manage germane and intrinsic loads at an appropriate level for learners using technology (Mayer, Sweller & Moreno, 2015; Wikipedia, 2020). These types of cognitive load, along with design principles and technology, comprise e-learning theory. E-learning theory belongs to the grand theory of Connectivism because it emphasizes how technologies can be used and designed to create new learning opportunities and to promote effective learning.

Previous Studies

Multimedia learning is one specific principle of e-learning theory, and it contends that deeper learning can be promoted using two formats among audio, visual, and text instead of one or three (Mayer, Sweller & Moreno, 2015). Previous studies relevant to e-learning theory have provided evidence that multimedia design principles can foster effective learning (Mayer & Moreno, 2003; Moreno & Mayer, 2007). For example, Mayer (1997) conducted several reviews of multimedia learning and found that multimedia instruction was effective. To be specific, Mayer (1997) reviewed eight studies on whether multimedia instruction was effective and found that students who were given a presentation with both verbal and visual explanations had a 75% higher median score for creative solutions on problem-solving transfer tests than students who experienced only verbal explanation. Ten studies reviewed by Mayer (1997) found that students showed scored more than 50% over the median on creative solution transfer tests when verbal and visual descriptions were concurrently employed.

Personalization is also an essential principle of e-learning theory. This principle suggests that presenting words in a conversational and informal style can help enhance effective learning (Mayer et al., 2015). Several studies have shown that personalization can be effective in learning. For example, Kartal’s (2010) study investigated the effectiveness of the design principle of personalization with 89 college students in an Istanbul university in Turkey by testing their computerized instructional content in a personalized informal style, personalized formal style, and neutral-formal style. The results showed that the amount of learning increased when the language style was formal and conversational.

Another study conducted by Kurt (2011) showed consistent results with Kartal’s (2010) study. Kurt (2011) examined the personalization effect with multimedia material in a formal style with 22 students and conversational style with 23 students. Using an achievement test, a cognitive load scale for both groups, and a questionnaire for the personalized group, Kurt found that students’ cognitive load scores in the personalized group were significantly different from and better than those in the non-personalized group. Besides, students in the personalized group said that the conversational style applied in the multimedia software inspired them to learn and they felt that a real human was talking to them. In addition, students showed a preference for multimedia materials.

Several other studies have also shown other design principles of e-learning theory to be effective. Some researchers studied the modality principle, which claims that the use of visuals accompanied by audio narration instead of on-screen text is more effective for learning (Mayer et al., 2015). For example, Moreno (2006) conducted a meta-analysis on modality effects. The results revealed significant learning benefits due to the modality principle across different media.

As can be seen from the above discussion, applying the principles of e-learning theory with its design principles can promote effective learning. Therefore, e-learning theory can be useful for teachers to design effective courses and for researchers to understand how effective learning with and through technology can happen.

Model of E-learning Theory

The model in Figure 1 demonstrates that concepts of three types of cognitive load and eleven empirical principles compose two constructs: cognitive load and design principles. These two constructs then combine to lead to the proposition of e-learning theory.

Concepts and Constructs

As noted previously, the three cognitive loads are intrinsic, germane, and extraneous based on the amount of mental effort. Intrinsic load is “the mental work imposed by the complexity of the content in your lessons and is primarily determined by your instructional goals” (Clark et al., 2005, p.9). Germane load is “mental work imposed by instructional activities that benefit the instructional goal” (Clark et al., 2005, p.11). Extraneous load is “the mental work that is irrelevant to the learning goal and consequently wastes limited mental resources” (Clark et al, 2005, p.12). Together these form the construct “cognitive load.”

E-learning theory is also composed of principles that can be integrated into instructional design; they that demonstrate “how educational technology can be used and designed to promote effective learning” (Wang, 2012, p.346). The eleven principles of the model that can promote effective learning are:

Multimedia principle: Using two formats of audio, visual, and text instead of using one or three.

Modality principle: Explaining visual content with audio narration instead of on-screen text.

Coherence principle: Avoiding irrelevant videos and audio.

Contiguity principle: Aligning relevant information to corresponding pictures concurrently.

Segmenting principle: Managing complicated content by breaking a lesson into small parts.

Signaling principle: Offering signals for the narration, such as arrows, circles, and highlights.

Learner control principle: Allowing the learner to control their learning pace.

Personalization principle: Presenting words in a conversational and informal style.

Pre-training principle: Providing descriptions or explanations for key concepts in a lesson before the main procedure of that lesson.

Redundancy principle: Presenting visuals with audio or on-screen text but not both.

Expertise effect: Considering that design principles may have a different effect on learners with various amounts of prior knowledge.  (Clark & Mayer, 2016; Mayer, 2003; Mayer & Moreno, 2003; Mayer et al., 2015)

Together, these eleven principles form the construct “design principles.”

Overall, the ideas of cognitive load and design principles can be integrated to reduce extraneous cognitive load and manage germane and intrinsic loads by making it easier for learners’ brains to handle the amount of information and processing that they must do during instructional tasks.

Proposition 

Based on the concepts and constructs, the model ends with the proposition, that if teachers design principled tasks with educational technologies that reduce extraneous cognitive load and manage germane and intrinsic load at appropriate levels for students, they can learn effectively (Mayer, Sweller & Moreno, 2015).

Possible  Ways to Use the Model

Several possible ways exist for using this model in research and practice. For example, researchers can use this model to better understand how design principles can be integrated in instruction to promote effective learning. Researchers can also conduct studies using the e-learning theory model to describe the design principles in learning contexts. In addition, this model can also help researchers address the following topics:

How research-based e-learning methodologies can be used to create an effective e-learning course.

How teachers can minimize extraneous load and manage intrinsic load to help effective learning.

Which design principles could contribute most to effective student learning.

Furthermore, teachers could apply the e-learning theory model in their classrooms to create effective e-learning courses. For example, teachers can help students manage their intrinsic cognitive load by splitting the content so that students can acquire knowledge step by step (Clark et al., 2005). Teachers can also scaffold students with small portions of new content gradually so that students can control their learning in a self-paced e-learning environment (Clark et al., 2005). Further, teachers can use basic digital communication tools with visuals, text, and audio to demonstrate learning content in ways that can help to reduce students’ intrinsic cognitive loads. In addition, teachers can apply effective graphics, audio, and text to minimize redundant content, concentrate on important content, and offer performance assistance to increase external memory. More examples of how teachers can apply e-learning theory in classrooms include:

Reducing extraneous cognitive load by avoiding irrelevant audio or complex visuals to describe complicated text (the coherence principle)

Managing intrinsic cognitive load by segmenting content into small parts and using pretraining to teach concepts and facts separately (the segmenting principle).

Fostering germane cognitive load by adding practice activities and relevant visuals (the modality principle) (Clark & Mayer, 2016).

There is no need to use all eleven principles to enhance students’ learning. Specific design principles can be used in different situations, depending on teachers’ instructional objectives and students’ learning objectives.

E-learning theory is about designing educational technology use to promote effective learning by reducing extraneous cognitive load and managing germane and intrinsic loads at students’ appropriate levels. It can be challenging for teachers to design tasks at an appropriate level for students; the e-learning theory model can help teachers understand how cognitive load can be categorized and combined with design principles to make effective learning with technology happen.

Clark, R.C., & Mayer, R.E. (2016).  E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning  (4th ed.). John Wiley & Sons, Inc.

Clark, R. C., Nguyen, F., & Sweller, J. (2005).  Efficiency in learning: Evidence-based guidelines to manage cognitive load . Pfeiffer.

David, L. (2015, December). E-learning Theory (Mayer, Sweller, Moreno). Learning Theories . https://www.learning-theories.com/e-learning-theory-mayer-sweller-moreno.html.

E-learning theory. (2020, April 11). In  Wikipedia . https://en.wikipedia.org/wiki/E-learning_(theory)

Kartal, G. (2010). Does language matter in multimedia learning? Personalization principle revisited.  Journal of Educational Psychology ,  102 (3), 615.

Kurt, A.A. (2011). Personalization principle in multimedia learning: Conversational versus formal style in written word.  TOJET: The Turkish Online Journal of Educational Technology ,  10 (3), 185-192.

Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions?  Educational Psychologist ,  32 (1), 1–19.

Mayer, R. (2003). Elements of a science of e-learning.  Journal of Educational Computing Research ,  29 (3), 297–313.

Mayer, R.E., Moreno, R., & Sweller, J. (2015). E-learning theory . https://www.learning-theories.com/e-learning-theory-mayer-sweller-moreno.html.

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning.  Educational Psychologist, 38 (1), 43-52.

Moreno, R. (2006). Does the modality principle hold for different media? A test of the method‐affects‐learning hypothesis.  Journal of Computer Assisted Learning ,  22 (3), 149-158.

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments.  Educational Psychology Review ,  19 (3), 309-326.

Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later.  Educational Psychology Review , 31( 2 ), 261–292.

Wang, V. C. (2012). Understanding and promoting learning theories.  International Journal of Multidisciplinary Research and Modern Education, 8 (2), 343-347.

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Article contents

Online learning.

• Lisa Marie Blaschke Lisa Marie Blaschke Carl von Ossietzky University
•  and  Svenja Bedenlier Svenja Bedenlier Friedrich-Alexander-University Erlangen-Nürnberg
• https://doi.org/10.1093/acrefore/9780190264093.013.674
• Published online: 30 April 2020

With the ubiquity of the Internet and the pedagogical opportunities that digital media afford for education on all levels, online learning constitutes a form of education that accommodates learners’ individual needs beyond traditional face-to-face instruction, allowing it to occur with the student physically separated from the instructor. Online learning and distance education have entered into the mainstream of educational provision at of most of the 21st century’s higher education institutions.

With its consequent focus on the learner and elements of course accessibility and flexibility and learner collaboration, online learning renegotiates the meaning of teaching and learning, positioning students at the heart of the process and requiring new competencies for successful online learners as well as instructors. New teaching and learning strategies, support structures, and services are being developed and implemented and often require system-wide changes within higher education institutions.

Drawing on central elements from the field of distance education, both in practice and in its theoretical foundations, online learning makes use of new affordances of a variety of information and communication technologies—ranging from multimedia learning objects to social and collaborative media and entire virtual learning environments. Fundamental learning theories are being revisited and discussed in the context of online learning, leaving room for their further development and application in the digital age.

• online learning
• online distance education
• digital media
• learning theory

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• Published: 18 November 2023

Students’ online learning adaptability and their continuous usage intention across different disciplines

• Zheng Li 1 ,
• Xiaodong Lou 2 ,
• Minwei Chen 3 ,
• Siyu Li 1 ,
• Cixian Lv 4 ,
• Shuting Song 4 &
• Linlin Li 4  

Humanities and Social Sciences Communications volume  10 , Article number:  838 ( 2023 ) Cite this article

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• Science, technology and society

Online learning, as a pivotal element in modern education, is introducing fresh demands and challenges to the established teaching norms across various subjects. The adaptability of students to online learning and their sustained willingness to engage with it constitute two pivotal factors influencing the effective operation of online education systems. The dynamic relationship between these aspects may manifest unique traits within different academic disciplines, yet comprehensive research in this area remains notably scarce. In light of this, this study constructs an Adaptive Structural Learning and Technology Acceptance Model (ASL-TAM) with satisfaction towards online teaching as the mediating variable to investigate the impact and mechanism of online learning adaptivity on continuous usage intention for students from different disciplines. A total of 11,832 undergraduate students from 334 universities in 12 disciplinary categories in mainland China were selected, and structural equation modeling was used for analysis. The results showed that the ASL-TAM model could be fitted for all 12 disciplines. The perceived ease of use, perceived usefulness, and system environment adaptability dimensions of online learning adaptivity significantly and positively affect satisfaction towards online teaching and continuous usage intention. Satisfaction towards online teaching partially mediates the relationship between online learning adaptivity and continuous usage intention. There were significant differences in the results of the single-factor analysis of the observed variables for the 12 disciplines, and the path coefficients in the ASL-TAM model fitted for each discipline were also significantly different. Compared to the six disciplines under the science, technology, engineering, and mathematics (STEM) category, six disciplines under the humanities category exhibited more significant internal differences in the results of the single-factor analysis of perceived usefulness and the path coefficients for satisfaction towards online teaching. This research seeks to bridge existing research gaps and provide novel guidance and recommendations for the personalized design and distinctive implementation of online learning platforms and courses across various academic disciplines.

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The unified theory of acceptance and use of DingTalk for educational purposes in China: an extended structural equation model

Yukun Hou & Zhonggen Yu

Nudge or not, university teachers have mixed feelings about online teaching

Sanchayan Banerjee, Beatriz Jambrina-Canseco, … Jenni Carr

Introduction

With the rapid development of information technology, online learning has become an integral part of modern education. China possesses the largest scale of higher education system and online learning course system globally (National Bureau of Statistics ( 2020 )). However, despite the widespread adoption of online learning platforms, there remain controversies surrounding students’ engagement, satisfaction, and willingness to continue using them. Therefore, researching how to enhance students’ willingness to persist in using online learning platforms is of paramount importance for the development and promotion of online learning. In recent years, scholars have increasingly focused on the factor of students’ online learning adaptability when studying the effectiveness of online learning for students and their willingness to continue using online platforms.

Prior studies have indicated that the overall level of adaptability to online learning among college students is relatively low (Luo, Huang ( 2012 )), and adaptability often becomes a critical factor determining the quality of learning and academic assessment in an online learning environment (D’errico et al., ( 2018 )). However, there is currently insufficient research evidence to fully understand the specific mechanisms through which online learning adaptability affects willingness to persist in using online platforms, necessitating further empirical research.

Moreover, given the extensive use and profound influence of online learning technologies in diverse academic fields (Chikwa et al., ( 2015 )), alongside the marked disparities in online learning outcomes across these disciplines (Ieta et al., ( 2011 )), delving into the intricate interplay between students’ online learning adaptability and their inclination to persist in using these tools across various domains becomes particularly instructive. It can provide valuable insights for crafting precise and efficacious online learning strategies and pedagogical models aimed at enhancing student learning outcomes and bolstering students’ satisfaction with online education.

As such, this study aims to investigate the impact of online learning adaptability on the willingness to persist in using online platforms among students from different disciplines while exploring the potential mediating effect of their satisfaction towards online teaching. This study randomly selected 11,832 valid samples from 256,504 students attending online learning in 12 disciplines across 334 universities in mainland China. Using structural equation modeling, the study analyzed the comprehensive impact of students’ online learning adaptability on their continued use intention of online learning. The study also analyzed the possible mediating effects of satisfaction towards online teaching among the 12 disciplinary categories in the “Degree Granting and Talent Training Discipline Catalog” issued by the Ministry of Education of China.

Theoretical foundation and research hypotheses

Adaptive online learning and continuance intention and their influencing factors.

Continuous usage intention is originated from the tracking and evaluation of the continuous use of software programs. It refers to the user’s decision to continue using a software application and the frequency of use based on the overall perception of the application. Continuous usage intention is one of the most important user indicators for judging the software system’s life cycle. This study applies this factor in the context of online learning research, and forms the concept of “continuance intention of online learning,” which is defined as learners’ intentions to continue choosing online learning as the primary learning method. This study seeks to determine whether students are willing to continue using this type of learning after a certain period of time.

In contrast to Daumiller et al. ( 2021 ), who suggest that teachers’ goals and attitudes have a critical impact on students’ continuance intention to use online learning, Yao et al. ( 2022 ) believe that the key factor affecting continued use of online learning is students’ self-awareness, which is closely related to their adaptability to online learning. Online learning adaptability refers to students’ ability to adapt to the learning environment by adjusting their learning strategies and adopting adaptive behaviors when using online learning platforms or systems. In the 1980s, Davis ( 1986 ) drew on the Theory of Reasoned Action to propose the Technology Acceptance Model (TAM). TAM is primarily used to predict the extent to which individuals are inclined to accept, use, or reject new information technologies (Rogers, 2005 ). Given that online learning adaptability can help students overcome difficulties and challenges in the learning process, increasing their acceptance and depth of use of online learning, students’ adaptability to online learning platforms or systems is likely to be one of the important factors influencing their decision to continue using online platforms.

Online learning adaptability is a complex, multidimensional concept. Generally, it is considered the ability of students to adjust their learning strategies, behaviors, attitudes, goal setting, and resource utilization to adapt to new learning conditions and requirements (Kizilcec et al., 2015 ). This includes adaptability in areas such as technical proficiency, self-management skills, and information literacy. Among these, the adaptability of university students to online learning primarily depends on their familiarity with the technology tools they use. Therefore, mastering online learning platforms, social media, and digital tools can enhance students’ adaptability to online learning (Selwyn, 2011 ). Additionally, in terms of instructional design, the design of online courses has a significant impact on students’ adaptability. Clear learning objectives, organized content, and diverse teaching methods contribute to improving students’ adaptability (Picciano, 2017 ). Providing effective technical support and assistance channels can alleviate students’ technological difficulties and enhance their adaptability to online learning (Johnson & Adams, 2011 ).

In analyzing the issues of online learning adaptability and acceptance, TAM provides several foundational factors, such as perceived ease of use, perceived usefulness, satisfaction, and self-efficacy (Cakır, Solak ( 2015 )). Perceived usefulness and perceived ease of use are generally considered the two most essential variables (Martins et al., 2014 ). Perceived usefulness refers to the degree to which users believe that using a particular information technology enhances their work efficiency, while perceived ease of use refers to users’ perception of how easy it is to operate a specific information technology (Davis, 1989 ). Alharbi and Drew ( 2014 ) argue that perceived ease of use and perceived usefulness in the TAM model significantly positively influence students’ intentions to use online learning. Therefore, this study proposes the following hypotheses:

H1a: The perceived usefulness dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

H1b: The perceived ease of use dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Apart from perceived ease of use and perceived usefulness, there is still no consensus on other important factors influencing continuance intention, especially regarding the strength and mechanisms of different factors (Joo et al., 2011 ). Liu et al. ( 2010 ) suggests that reasonable external extension variables can effectively predict users’ intentions to use online learning. Bazelais et al. ( 2018 ) and Xu, Lv ( 2022 ) also propose considering the additional effects of external influencing variables in the study of continuance intention. As a frontier and hot topic in online learning research (Jovanovic, Jovanovic ( 2015 )), the theory of Adaptive Learning Systems (ALS) from cognitive psychology proposes the concept of “human-machine interaction adaptability,” which includes two aspects: human adaptation to technology and technology adaptation to humans. The latter relies on the “learner model” to automatically analyze learners’ cognitive levels and learning styles, and then feedback to the former to enhance learners’ learning progress and effectiveness (Retalis, Papasalouros ( 2005 )). Social Cognitive Theory also suggests a similar viewpoint, indicating that students’ adaptability is largely influenced by multiple social contexts. A substantial amount of research on ALS also demonstrates that ALS, as a scientific learning medium, can more actively meet students’ learning needs (How, Hung ( 2019 )), help correct the learning paths generated by students’ autonomous learning habits (Nihad et al., ( 2017 )), and effectively improve students’ learning adaptability (Zulfiani et al., ( 2018 )). This study believes that online learning adaptability is a comprehensive, two-way process for students to adapt to changes in the learning environment through self-perception and for software systems to adapt to user needs systematically. It includes three variables: perceived ease of use, perceived usefulness, and system environment adaptability, with the latter referring to the functional adaptability of learning software systems to different learning styles of learners. Therefore, this study proposes the following hypothesis:

H1c: The system environment adaptation dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Satisfaction towards online teaching and its possible mediating role

Prior research has suggested that satisfaction towards online teaching and perceived usefulness are considered core components in evaluating the effectiveness of online learning (Menon, Seow ( 2021 )), as they relate to the quality of online courses and students’ performance (Kuo et al., 2014 ). Scholars attach great importance to the research on the relationship between students’ satisfaction towards online teaching and their continued usage intention, with satisfaction being considered a key element affecting students’ continued usage intention and behavior (Lee, 2010 ).

Among the potential factors contributing to positive adaptability in online learning, perceived usefulness and perceived ease of use are recognized as two significant factors affecting satisfaction (Huang, 2020 ). Additionally, factors influencing satisfaction can indirectly impact the intention to continue using the system (Bhattacherjee, 2001 ). Furthermore, online educational platforms with robust system adaptability can provide a more stable network connection, higher-quality learning resources, and a more diverse array of learning pathways. Moreover, they can deliver personalized learning support and teaching resources tailored to individual student needs and learning characteristics. This assists students in overcoming learning challenges and enhances teaching effectiveness, ultimately leading to greater teaching satisfaction. Notably, technological innovations introduced by ALS effectively enhance learners’ perceived quality and have a positive indirect influence on teaching satisfaction (Janati et al., ( 2018 )). Therefore, the following hypotheses are proposed:

H2a: The perceived usefulness dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2b: The perceived ease of use dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2c: The system environment adaptation dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

It is generally believed that students’ satisfaction towards online teaching can refer to the indicator system proposed by the research on satisfaction towards classroom teaching, comprehensively evaluating common teaching factors such as course design, learning objectives, teaching methods, teacher qualifications, and interactive experiences. Palmer, Holt ( 2010 ) believe that the research on students’ satisfaction towards online teaching should pay more attention to the unique factors of the online teaching environment, such as teaching interactivity, technical proficiency, and online self-assessment. Bolliger and Wasilik ( 2009 ) also believes that we should start from the key participants in the online environment, focusing on the impact of various aspects such as teachers’ information technology application, students’ communication level, and school policy and logistical support. Kurucay and Inan ( 2017 ) opine that the key factor influencing online learning effectiveness is the interaction between learners. Regarding the main factors influencing learners’ satisfaction towards online teaching, Kranzow ( 2013 ) believe that the essential factors are related to teacher’s online course design level and the ability to respond to student needs in a timely manner. Hogan and McKnight ( 2007 ) believe that factors such as the teaching environment and technical support are the main reasons for influencing satisfaction towards online teaching. In addition, there are significant differences in the predicting factors for the acceptance of online learning and satisfaction towards online teaching among university students from different countries (Piccoli et al., 2001 ). Based on the above research, this study will further analyze the factors influencing learners’ satisfaction towards online teaching in the online learning environment, and propose the following hypothesis:

H3: Students’ satisfaction towards online teaching positively affects their continued usage intention.

Previous studies have shown that students’ satisfaction towards online teaching is likely to be influenced by their learning adaptability, and at the same time affects their intention to continue attending online learning (Waheed, 2010 ). Therefore, students’ satisfaction towards online teaching may play a special mediating role between students’ learning adaptability and their continuance intention. Yeung and Jordan ( 2007 ) found that factors such as perceived usefulness, perceived ease of use, and service quality evaluation that affect online learning satisfaction also have a positive impact on students’ continuance intention. Young ( 2013 ) reached similar conclusions and believed that students’ satisfaction towards online teaching plays a mediating role in the process of affecting their continuance intention. However, there are also different views about this topic. For example, Troshani et al. ( 2011 ) found that although perceived ease of use has a significant impact on learners’ usage satisfaction, it does not have a significant impact on their continuance intention. Therefore, the mediating effect of learning adaptability on learners’ continuance intention may be extremely important and needs to be verified through empirical research. Therefore, this study proposes that students’ satisfaction towards online teaching plays a mediating role between their online learning adaptability and continued usage intention. The specific hypotheses are as follows.

H4a: Students’ satisfaction towards online teaching plays a mediating role between perceived usefulness and their continued usage intention.

H4b: Students’ satisfaction towards online teaching plays a mediating role between perceived ease of use and their continued usage intention.

H4c: Students’ satisfaction towards online teaching plays a mediating role between system environment adaptability and their continued usage intention.

Designing the model framework

As mentioned earlier, it is feasible to use the TAM model to study the sustained usage intention of online learning, and its explanatory power has been verified by empirical studies (Dziuban et al., 2013 ). However, with the increasing complexity of the online environment, the traditional TAM model may encounter issues with low reliability and validity in explaining complex user environments. Therefore, the academic community has been continuously selecting, combining, and adjusting the basic components of the TAM model. Davis et al. ( 1992 ) pointed out that when using TAM theory, multiple external variables, including intrinsic motivation, should be considered, as they may have complex effects on endogenous variables and behavioral intentions. Farahat ( 2012 ) found that, in addition to perceived usefulness and perceived ease of use, student attitudes and social influences in online learning are also important factors that influence students’ willingness to engage in online learning. Therefore, based on the Technology Acceptance Model (TAM) and the Adaptive Structural Learning Model (ALS), this study combines them to construct the Adaptive Learning and Technology Acceptance Model (ASL-TAM model; see Fig. 1 ) as follows:

In ASL-TAM model, online learning adaptability consists of three factors, which are hypothesized to predict continued usage intention and satisfaction towards online teaching.

Methodology

Data source.

The data for this study were collected from an online learning survey conducted by a Teacher Development Centre of a public university (IRB No. NB-HEC-20200328L) in mainland China from 2020 to 2021. The survey was distributed to students through the academic affairs offices of various schools. Additionally, two lie-detection questions were included in the questionnaire to ensure the validity and reliability of the data. Each student account could only save one survey form. In other words, if the same account answered multiple times, the results of the last response would automatically overwrite the previous ones. A total of 256,504 data sets were collected from 334 universities. Among the surveyed students, there were 110,411 males (43%) and 146,093 females (57%). In terms of geographical distribution, 110,919 students (43.2%) were from the eastern region of China, 106,007 (41.3%) were from the central region, and 38,847 (15.1%) were from the western region. The surveyed students were also classified into different academic disciplines, including 11,086 in philosophy, 20,953 in economics, 7420 in law, 17,100 in education, 24,658 in literature, 1201 in history, 29,517 in natural science, 76,301 in engineering, 5295 in agriculture, 11,161 in medicine, 24,583 in management, and 27,229 in arts. A sample of 1000 student questionnaires was randomly selected from each academic discipline, resulting in a total of 12,000 data sets. The sample was cleaned based on criteria such as lie-detection questions, response times (data below 5 min or above 20 min were removed based on the statistical “3σ rule”), age (data below 15 years old or above 25 years old were removed based on the statistical “3σ rule”), school names (data with randomly filled school names were removed), and whether online learning was used (data indicating no usage were removed). In total, 162 samples were cleaned, resulting in 11,832 valid samples (with 986 for each of the 12 academic disciplines).

Instrumentation

This study was conceptualized based on TAM from the theory of rational behavior and the ALS theory from cognitive psychology. These theories were employed to investigate the underlying mechanisms of the impact of online learning adaptability on users’ continuance intention. In this regard, we consulted the research findings of scholars such as Davis ( 1993 ), Igbaria ( 1990 ), Ajzen & Fishbein ( 1980 ), Chen and Tseng ( 2012 ), among others. The questionnaire consisted of 33 items measuring five variables (see Table S1 for the complete questionnaire): perceived usefulness (11 items), perceived ease of use (3 items), adjustment to system environments (10 items), satisfaction of teaching (7 items), and continuance intention (2 items). The overall reliability of the questionnaire was tested using the Cronbach’s alpha coefficient (0.924), KMO (0.937), and Bartlett’s sphericity test ( p  < 0.001) in SPSS 25.0 software, indicating that the questionnaire data were reliable and suitable for exploratory factor analysis (EFA). Three principal components were extracted for perceived usefulness (PU): teaching resources (PU_TR), classroom teaching (PU_CT), and teaching evaluation (PU_TE). Three principal components were also extracted for perceived ease of use (PEU): technical training (PEU_TT), pedagogical training (PEU_PT), and proficiency levels (PEU_PL). Three principal components were extracted for system environment adaptation (SEA): technical service (SEA_TSER), teaching support (SEA_TSUP), and policy support (SEA_PS). Three principal components were extracted for satisfaction with online teaching (ST): effectiveness of teaching (ST_TE), teaching experience (ST_TEXP), and learning outcomes (ST_LO). Two principal components were extracted for continuance intention (CIN): online mode (CIN_ON) and blended mode (CIN_BL). Perceived usefulness, perceived ease of use, and system environment adaptation were combined to form the independent variable “adaptive structural learning (ASL)” in this study, while satisfaction towards online teaching was the hypothesized mediating variable and continuance intention was the dependent variable. The academic disciplines were treated as control variables. The perceived usefulness and perceived ease of use scales were adapted from Davis ( 1993 ), the system environment adaptation scale was adapted from Igbaria ( 1990 ), the satisfaction towards online teaching scale was adapted from Ajzen and Fishbein ( 1980 ), and the continuance intention scale was adapted from Chen and Tseng ( 2012 ).

Research method

Descriptive statistics were conducted on the data of 12 disciplines using SPSS 25.0 software, and model construction, model revision, and model interpretation were carried out using AMOS 24.0.

Reliability analysis

Reliability analysis was conducted on the 14 latent variables across the 12 disciplines using SPSS 25.0 software (see Table 1 for results). The results showed that the alpha values of the observation variables based on standardized items were all greater than or equal to 0.9, indicating that the questionnaire of the 12 disciplines had high reliability. During reliability analysis, the scores of the latent variables calculated using the mean method also had considerable reliability, indicating excellent data reliability. The data of the 12 disciplines were suitable for further structural model testing.

Common method bias (CMB) test

The data used in this study were collected through self-reporting methods on the internet, which may have CMB. Before formal data analysis, a Harman single-factor test was conducted to examine common method bias. First, exploratory factor analysis (unrotated) was performed using SPSS 25.0 software. The results showed that the first principal component accounted for 29.21% of the variance, which did not meet the 40% threshold.

One-way ANOVA of disciplinary variables

One-way ANOVA analysis was conducted on the observation variables of 12 disciplines. According to the results in Table 2 , if the 12 disciplines are viewed as a whole, the evaluation of perceived ease of use (3.62) is higher than system environment adaptation (3.60) and perceived usefulness (3.47). The satisfaction towards online teaching (3.47) is higher than continuous usage intention (3.44). Perceived usefulness is the main weak link of online learning adaptability, and the main observation variable that causes the low value of perceived usefulness is teaching evaluation (3.26). The lowest discipline evaluation value comes from philosophy (3.41). The observation variable with the lowest evaluation value in perceived ease of use is technical training (3.58), and the observation variable with the lowest evaluation value in system environment adaptation is technical service (3.53). The observation variable with the lowest evaluation value in the satisfaction towards online teaching is effectiveness of teaching (3.28). All 14 observation variables of the 12 disciplines showed significant inter-group differences ( p  < 0.001), indicating that there were general differences in the evaluation outcomes among the observation variables of different disciplines.

Correlation analysis among variables

To explore the relationships between the variables, a correlation analysis was performed. As shown in Table 3 , there were significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation. There were also significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation with the mediating variables of satisfaction towards online teaching and continued usage intention. Additionally, there was a significant positive correlation ( p  < 0.001) between satisfaction towards online teaching and continued usage intention.

Model construction and fitting

Based on the ASL-TAM model developed in Fig. 1 , a structural equation model was constructed using AMOS 24.0 software, and the initial model was estimated using maximum likelihood. Taking the subject of physics as an example, the results of the initial model fit showed that the correction index MI value of the residual path [e2 < -->e3] was relatively large. Therefore, the initial model was corrected by adding the [e2 < -->e3] residual path, and all path p -values were less than 0.05 after the correction, indicating statistical significance. The fitted model is shown in Fig. 2 .

The validated ASL-TAM model for the subject of physics demonstrated good fit, with most hypotheses being substantiated.

The fitted model for the subject of physics showed good results. The same fitting method was used for the other 11 subjects, and the results showed that all 12 models could be fitted, and the 12 fitting goodness-of-fit indices were within the standard range. Therefore, the ASL-TAM model can be used for relevant evaluation and prediction work (see Table 4 for goodness-of-fit indices).

Path analysis results of fitted models

The path coefficients of the structural equation can reflect the mutual relationships between latent variables and between latent variables and observed variables. The path coefficients between variables after the fitting of the 12 subjects are shown in Table 5 . First, the ASL-TAM models of all 12 subjects can achieve overall convergence. The path coefficients of satisfaction towards online teaching (ST) on continuous usage intention (CIN) are all significant in all 12 subjects, verifying research hypothesis H3. Second, the three paths “perceived ease of use (PEU) → continuous usage intention (CIN)”, “perceived usefulness (PU) → continuous usage intention (CIN)”, and “system environment adaptation (SEA) → continuous usage intention (CIN)” all display significant path coefficients and can be fitted into the ASL-TAM model, indicating that online learning adaptability and its three dimensions all have a significant positive impact on continuous usage intention (CIN), substantiating research hypotheses H1, H1a, H1b, and H1c. Third, the three paths “perceived ease of use (PEU) → satisfaction towards online teaching (ST)”, “perceived usefulness (PU) → satisfaction towards online teaching (ST)”, and “system environment adaptation (SEA) → satisfaction towards online teaching (ST)” all display significant path coefficients, indicating that online learning adaptability and its three dimensions all have a significant positive impact on satisfaction towards online teaching (ST), verifying research hypotheses H2, H2a, H2b, and H2c. Additionally, the path “Satisfaction towards online teaching (ST) → continuous usage intention (CIN)” is displayed with a significant path coefficient in all 12 subjects, indicating that “satisfaction towards online teaching (ST)” has a partial mediating effect between “perceived ease of use (PEU)”, “perceived usefulness (PU)”, “system environment adaptation (SEA)” and “continuous usage intention (CIN)”, verifying research hypotheses H4, H4a, H4b, and H4c.

This study confirms the positive impact of online learning adaptability on users’ intention to continue using the platform. This aligns with previous research findings that students’ adaptation to a course significantly affects their learning outcomes (Manwaring et al., 2017 ). Unlike most studies that only focus on students’ one-way adaptation to the teaching system, this study confirms that both students’ “perceived adaptation” to the system and the system’s “adaptive needs” to the students are equally important and should be considered as a whole. When students’ perceived position in the system matches the target characteristics predicted by the system, they will rate the teaching activities higher (Bretschneider et al., 2012 ).

This study also confirms the positive impact of online learning adaptability on satisfaction towards online teaching, which is in line with previous research that adaptability is an important indicator of students’ learning satisfaction, perceived utility, and intention to continue learning (Machado, Meirelles ( 2015 )). Therefore, adaptability should be the logical starting point for designing online learning systems. At the same time, enhancing the intelligence perception of “human-computer interaction” and improving the teaching adaptivity of “teacher-student interaction” are important directions for enhancing users’ intention to continue using online learning and improving the overall quality of online learning.

This study also confirms the positive impact of satisfaction towards online teaching on users’ intention to continue using the platform, and the TAM model is applicable in evaluating satisfaction and intention to continue using in 12 subject areas. The adaptive structural learning and technology acceptance model fit successfully in all 12 subject areas. This confirms that the TAM model can be used to explain the factors that influence learners’ acceptance of online learning (Venkatesh, Davis ( 2000 )), and the core structure of TAM has a significant impact on users’ intention to continue using (Natasia et al., 2022 ).

Furthermore, this study confirms that satisfaction towards online teaching partially mediates the relationship between online learning adaptability and users’ intention to continue using the platform. The ASL-TAM model developed in this study reveals that there are expression differences in the factors that affect satisfaction towards online teaching and users’ intention to continue using in the 12 subject areas, and the ASL-TAM model can explore the deep path reasons for the expression differences in the factors affecting users’ intention to continue using (Al-Azawei, Lundqvist ( 2015 )), and then analyze the educational goals and methods paths for implementing online learning in different subjects.

This study has three contributions. First, the study found that perceived usefulness (PU) (3.47) was lower than system environment adaptation (SEA) (3.60) and perceived ease of use (PEU) (3.62). The continuous usage intention (CIN) (3.44) was lower than satisfaction towards online teaching (ST) (3.47). The main observed variables leading to a low evaluation of perceived usefulness (PU) were teaching evaluation (PU_TE) (3.26) while the lowest evaluated variable in perceived ease of use (PEU) was technology training (PEU_TT) (3.58). In system environment adaptation (SEA), the lowest evaluated variable was technical service (SEA_TSER) (3.53) while the lowest evaluated variable in satisfaction towards online teaching (ST) was teaching effectiveness (ST_TE) (3.28). This indicates that online education in mainland China is still in the early stage of hardware facilities configuration and teaching technology training. The continuous usage intention (CIN) is generally weak, possibly due to the weak links in the early adaptation to online learning, which affects the evaluation of satisfaction towards online teaching (ST), leading to a weaker overall continuous usage intention (CIN). Online learning needs more specific and effective project support (Ramadhan et al., 2021 ).

Second, the study confirms that satisfaction towards online teaching (ST) plays a partial or complete mediating effect between perceived ease of use (PEU), perceived usefulness (PU), system environment adaptation (SEA) and continuous usage intention (CIN), which confirms previous research conclusions. That is, user satisfaction is a key antecedent to influence user intention to continue use and behavior (Igbaria et al., 1997 ). There are many possible factors that influence continuous usage intention (CIN) of a teaching method, but among various factors, satisfaction towards online teaching (ST) of the student population is the “central factor”, especially for online education, learner satisfaction is considered a key factor for teaching success (Joo et al., 2011 ). It is also important to strengthen system environment adaptation (SEA) based on human-computer interaction, as online learning requires an attractive and motivational external environment (Agyeiwaah et al., 2022 ), and satisfaction may vary due to internet experience (Reed, 2001 ).

Thirdly, this study confirms the significant differences in satisfaction towards online teaching (ST) and continuous usage intention (CIN) between STEM and humanities disciplines. Influenced by the early college entrance examination system, China has conventionally classified disciplines into STEM and humanities, similar to the “arts” and “science” branches in the subject guidelines of Western universities. The classification not only affects the disciplines but also results in significant differences in academic literacy among students in different fields. This study found that compared to STEM disciplines (such as natural science, engineering, agriculture, medicine, and management), the six traditional humanities disciplines, namely philosophy, law, education, literature, history, and economics, showed extremely significant differences in perceived usefulness (PU), which may be due to the difference in teaching style between humanities and STEM (Tuimur et al., 2012 ) and the peer cultural influence within the humanities. A study of nearly 500,000 online courses in the state of Washington in the United States has similar conclusions that students face greater difficulties in online learning in fields like English and social sciences, possibly due to the existence of “negative peer effects” in the online courses of these disciplines (Lv et al., 2022 ).

Implications

In order to enhance the satisfaction towards online teaching and continued usage intention of online education, this study proposes the following suggestions:

From the perspective of cognitive psychology, the differences in online teaching among different disciplines are mainly manifested in various aspects such as the cognitive perspectives and learning habits of students with different disciplinary backgrounds. From the standpoint of educational technology theory, there is a need for continuous development of multidimensional and multilevel teaching systems to adapt to the knowledge structures, teaching principles, and curriculum characteristics of different disciplines. Furthermore, constructivist learning theory emphasizes that teachers should assist students in improving their learning adaptability more actively and in constructing knowledge and meaning more proactively. This study empirically validates the above viewpoints and provides new discoveries. Research shows that there are significant differences in satisfaction towards online teaching and continued usage intention in online learning among different subjects, so different online learning for different subjects should be implemented. On the one hand, the convergence of online learning in different subjects should be grasped, and a wide-caliber, widely applicable teaching platform carrier should be constructed to effectively integrate different subject knowledge into the virtual classroom knowledge situation, and better promote the integration of knowledge and skills. On the other hand, attention should be paid to the objective differences of different subjects, and an online education system reflecting the advantages of different subjects should be designed according to the teaching contents of different subjects.

From the perspective of practicality, it is necessary to pay close attention to the significant differences among various disciplines in terms of subject content and learning objectives, teaching methods and learning activities, assessment and feedback methods, as well as the roles of teachers and technological support. It is important to actively develop teaching methods that are tailored to different disciplines, especially in the case of experimental courses. Compared with traditional classroom education, the important breakthrough of online learning is the more convenient and timely teaching feedback. Future online learning systems should create adaptive learning environments based on the different characteristics of learners (Park and Lee, 2003 ), and accelerate the construction of adaptive learning systems for college students with different learning methods in different subjects, which is an effective solution to the conflict between diversified subject needs and static teaching resources, and an important way to resolve the contradiction between diversified student levels and limited teaching resources. For science and engineering subjects, attention should be paid to improving the external environment of online learning, actively improving online learning performance evaluation, promoting industry-university-research cooperation, promoting demand docking, resource sharing, and complementary advantages, promoting industry-education integration and industry-university co-construction, and achieving win-win results for teachers and students. For humanities subjects, the technical support for each link of online learning should be improved, and more humanistic care should be reflected in interactive teaching support. Through more social integration, knowledge exploration-based social consultation can be promoted.

In terms of the broader external educational environment and technological development trends, we should emphasize the opportunities for educational technology innovation and industry-education integration brought about by the differential development of online teaching in various disciplines. Clearly, the issue of disciplinary differences presents challenges in terms of teaching organization and operation, but it also promotes opportunities for personalized learning, collaborative teaching, and diversified assessment. China is already a major player in online education, but it is not yet a powerhouse in this field. To unleash the educational value of online learning and expand its innovative significance, online education, represented by flipped classrooms and MOOCs, not only provides new teaching methods and educational pathways, but also brings innovative educational ideas and paradigms. Therefore, online education needs to emphasize the re-examination of external contexts, overcome the mechanical thinking of “100% replication of classroom education,” and explore new teaching paths and operating modes, providing teachers and students with more novel teaching experiences and promoting the comprehensive improvement of their knowledge, abilities, and qualities.

Limitations and future research

This study has two limitations. Firstly, to increase the credibility of the research conclusions, we have tried to increase the sample size, resulting in a relatively large number of universities involved in the study. These universities may have differences in their discipline settings and standards, which may introduce some errors that need to be addressed in future research. Secondly, previous studies have shown that factors such as the location of the participants, the level of their universities, and their academic year may affect their satisfaction with teaching. We were unable to eliminate these possible interferences in this study and will improve this in future research.

Data availability

The data presented in this study are available on request from the corresponding author. According to the regulation of the Ethics Committee of Ningbo University, the data are not publicly available due to ethical reasons as they contain personally identifiable information.

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This research was funded by the National Social Science Foundation (Education) Project, “Research on the Path and Mechanism of Universities Promoting Rural Entrepreneurship Education under the Background of Rural Revitalization” (grant No. BIA200204).

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Li, Z., Lou, X., Chen, M. et al. Students’ online learning adaptability and their continuous usage intention across different disciplines. Humanit Soc Sci Commun 10 , 838 (2023). https://doi.org/10.1057/s41599-023-02376-5

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Academic performance under COVID-19: The role of online learning readiness and emotional competence

1 University of Alabama, Tuscaloosa, AL 35487 USA

Wenjing Guo

2 Beijing Normal University, Beijing, China

3 Dalian Neusoft University of Information, Dalian, China

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The authors do not have permission to share the data used in this study.

The COVID-19 pandemic caused school closures and social isolation, which created both learning and emotional challenges for adolescents. Schools worked hard to move classes online, but less attention was paid to whether students were cognitively and emotionally ready to learn effectively in a virtual environment. This study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period. Two groups of students participated in this study, with 1,316 high school students ( Mean age = 16.32, SD = 0.63) representing adolescents and 668 college students ( Mean age = 20.20, SD = 1.43) representing young adults. Structural equation modeling was conducted to explore the associations among online learning readiness, emotional competence, and online academic performance during COVID-19 after controlling for pre–COVID-19 academic performance. The results showed that, for high school students, both online learning readiness and emotional competence were positively associated with online academic performance during COVID-19. However, for college students, only online learning readiness showed a significant positive relationship with online academic performance during COVID-19. These results demonstrated that being ready to study online and having high emotional competence could make adolescents more resilient toward COVID-19–related challenges and help them learn more effectively online. This study also highlighted different patterns of associations among cognitive factors, emotional factors, and online academic performance during COVID-19 in adolescence and young adulthood. Developmental implications were also discussed.

COVID-19, as a public health crisis, stimulated a subsequent education crisis in which the existing achievement gap, learning loss, and dropout rate were exacerbated due to school closures (Sahu, 2020 ; United Nations, 2020 ). To prevent COVID-19 transmission, educational institutions worldwide made massive efforts to shift from in-person to online teaching (Basilaia & Kvavadze, 2020 ; Chen et al., 2020 ; Daniels et al., 2021 ; Subedi et al., 2020 ). However, little is known about whether students were cognitively and emotionally ready to learn effectively online at the time of transition.

COVID-19 created learning challenges caused by changes in educational platforms, especially for adolescents. Adolescence is a time when peer influences expand (Knoll et al., 2015 ; Knoll et al., 2016 ). With the dramatic changes in adolescents’ “social brain,” these students have a stronger desire for social interaction and are more sensitive to social isolation (Blakemore, 2008 ; Steinberg, 2005 ; Yurgelun-Todd, 2007 ). Social interactions with teachers, peers, and others are crucial elements in adolescents’ learning experiences (Perret-Clermont et al., 2004 ). Therefore, students struggle to be cognitively engaged in class without the motivation of in-person interactions with teachers and peers during online learning (Kim & Frick, 2011 ; Zembylas et al., 2008 ). Moreover, the new platform delivers information in an entirely different way within a totally different environment (i.e., school vs. home), which requires students to use technology and communicate effectively virtually while resisting distractions in the new environment (Aguilera-Hermida, 2020 ; Chen & Jang, 2010 ; Ferrer et al., 2020 ). In short, learning effectively online was extremely challenging during the pandemic.

In addition, COVID-19–related mental health difficulties, such as loss of relatives, social isolation, and heightened stress and anxiety (Hamza et al., 2020 ; Son et al., 2020 ; Wang et al., 2020 ), made students’ academic lives even more challenging (Grubic et al., 2020 ; Liang et al., 2020 ; Thakur, 2020 ; Zhai & Du, 2020 ; Zhao, 2021 ). As mentioned above, adolescence is a developmental stage characterized by a particularly sensitive “social brain” (Blakemore, 2008 ), and it is a critical period for emotional competence development (Booker & Dunsmore, 2017 ; Trentacosta & Fine, 2010 ). As such, any interpersonal and social-emotional suffering is magnified for adolescents when compared to individuals in other developmental stages. Students during this developmental stage need to have higher emotional competence to cope with emotional distress effectively, allowing them to be more resilient to the challenges of the COVID-19 pandemic and perform better academically (Baba, 2020 ; Bao, 2020 ). Therefore, this study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period.

COVID-19 and online learning readiness

Online learning readiness refers to students’ preparation to learn effectively in an online environment (Demir Kaymak & Horzum, 2013 ; Wei & Chou, 2020 ). Although whether students are ready for the “novice” online learning environment of the COVID-19 pandemic is an ongoing question, some preliminary findings provide insight into this question. Within higher education, according to Chung et al. ( 2020 ), students were generally ready for online learning in Malaysia. However, other researchers claimed that students’ learning readiness was lacking (Widodo et al., 2020 ). In high school settings, students were found to have inadequate digital skills for online learning in Delhi (Bhaumik & Priyadarshini, 2020 ). Conversely, Dwiyanti et al. ( 2020 ) reported that most junior high school students in Indonesia were ready and only needed a few improvements. Considering that each institution, country, and researcher may have different standards of “being ready” for online learning, a more meaningful question is this: How did online learning readiness influence students’ academic performance during the COVID-19 pandemic?

Online learning readiness and academic performance

Facilitating academic success is especially important for adolescents and young adults because academic performance has significant implications for future career development (Negru-Subtirica & Pop, 2016 ; Van der Aar et al., 2019 ). The current pandemic is lowering adolescents’ academic motivation (Aboagye et al., 2020 ), inducing learning loss (Kuhfield & Tarasawa, 2020 ; Turner et al., 2020 ), and ultimately causing lower academic performance (Kuhfeld et al., 2020 ). This phenomenon is partly due to a lack of readiness for online learning. According to the OECD’s Programme in International Student Assessment (PISA), most adolescents from diverse countries (i.e., 15-year-olds in the 79 education systems in the PISA database) were not ready to learn online (Reimers & Schleicher, 2020 ).

Online learning is not purely about having a place or a computer with which to study. More importantly, it requires specific skills and online learning self-efficacy (Smith, 2005 ). Many studies have recognized the importance of students’ motivation in the online learning environment (e.g., Chen & Jang, 2010 ; Khalilzadeh & Khodi, 2021 ). One challenge of online learning readiness research is that researchers have used different constructs, some of which overlap with self-directed learning and motivation (e.g., Cigdem & Ozturk, 2016 ; Pintrich, 2000 ; Zimmerman, 2008 ). Based on previous studies and in an effort to distinguish online learning readiness from self-directed learning and motivation, the current study focused on the three most-used factors in the online learning readiness literature: computer and Internet self-efficacy, learners’ self-control in online contexts, and online communication self-efficacy (Hung et al., 2010 ; Yu, 2018 ).

Studies have indicated that these three online learning readiness factors are associated with students’ online academic performance. Computer and Internet self-efficacy concerns students’ confidence with computer and Internet use (Hatlevik et al., 2018 ; Torkzadeh et al., 2006 ). Having confidence in using Microsoft Office software or conducting Internet research enables online problem-solving, lessens the stress caused by technology, and improves academic performance (Compeau & Higgins, 1995 ; Eastin & LaRose, 2000 ; Tsai & Lin, 2004 ). Learners’ self-control in online contexts refers to students’ ability to avoid distractions from social media (e.g., Facebook or Instagram) and video games and to focus on online courses and assignments (Teng et al., 2014 ; Wang & Beasley, 2002 ). Finally, online communication self-efficacy reflects students’ willingness and confidence in online interactions with instructors and peers to deepen understanding, which benefits their learning outcomes and learning satisfaction (Roper, 2007 ; Yilmaz, 2017 ). Having computer and Internet self-efficacy, self-control in online contexts, and online communication self-efficacy assists students with the transition to the online learning environment (Miao et al., 2020 ). Ultimately, these three factors all contribute to students’ online learning performance.

Overall, online learning readiness has been shown to positively correlate with college students’ academic performance in the online learning environment (Davies & Graff, 2005 ; Lee & Choi, 2013 ; Yu, 2018 ). Moreover, research results have been consistent across studies in diverse college samples (Bernard et al., 2004 ; Joosten & Cusatis, 2020 ). However, before the current pandemic, the majority of online learning readiness studies focused on higher education. More studies are needed to address the role of online learning readiness in high school students’ online academic performance and to determine how to support high school students in preparing for online learning, especially during the COVID-19 pandemic.

COVID-19 and emotional competence

Beyond online learning preparedness (e.g., computer skills or self-control in an online learning environment) for virtual learning during the COVID-19 pandemic, students also need emotional competence to prepare them for the hectic world. Emotional competence is defined as an individual’s ability to express, regulate, and understand emotions (Denham et al., 2015 ; Saarni, 1999 , 2000 ). Special attention needs to be paid to adolescents’ emotional competence during the COVID-19 pandemic for two major reasons. First, emotional competence, as a crucial factor in academic performance (Brackett et al., 2012 ; Oberle et al., 2014 ; Rhoades et al., 2011 ) and effective functioning in adulthood (Kotsou et al., 2011 ; Takšić, 2002 ), are developed through socialization during adolescence (Valiente et al., 2020 ). With the unavoidable social isolation caused by COVID-19, adolescents have been shown to be less aware and less accepting of their own emotions (Hurrell et al., 2017 ; Valiente et al., 2020 ) and to have a harder time regulating their emotions (Casey et al., 2019 ; Cole, 2014 ). Indeed, several early works on COVID-19’s immediate impacts reported an increase in low emotional competence-related mental health issues in adolescents and young adults (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ).

Second, there is an urgent need for adolescents to be emotionally competent to deal with the extra emotional distress caused by COVID-19, including the experience of illness, loss of relatives, and financial difficulties during the pandemic (Li et al., 2021 ; Pan, 2020 ; Wathelet et al., 2020 ) as well as feelings of anxiety, depression, and sadness (Imran et al., 2020 ). Having high emotional competence would help students control and regulate their grief, sadness, and stress to cope with the new online learning environment more effectively (Baba, 2020 ; Moroń & Biolik-Moroń, 2020 ).

Emotional competence and academic performance

High emotional competence could not only lessen mental health issues but could also contribute to academic performance in both adolescent and young adult populations (Brackett et al., 2012 ; Harley et al., 2019 ; Parker et al., 2004 ). Low emotional competence is related to increased mental health problems (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ), which in turn interfere with academic performance (Dekker et al., 2020 ; Tembo et al., 2017 ). COVID-19 escalated this linkage because adolescents had a harder time regulating emotions due to social relationship changes (Akgül & Atalan Ergin, 2021 ; Mathews et al., 2016 ) and experienced higher levels of emotional distress caused by COVID-19-related issues (Magson et al., 2021 ).

According to recent research, students with a better ability to perceive and regulate emotions had higher online learning readiness levels and were more resistant to online distractions (Engin, 2017 ), so they were more likely to have better academic performance in an online learning setting (Artino Jr & Jones II, 2012 ; Kim & Pekrun, 2014 ). However, most emotional competence studies have been conducted in traditional face-to-face learning settings and focused on specific emotions, so it is necessary to test the role of emotional competence in online settings, especially during the current pandemic. Moreover, emotional competence plays different roles in adolescents’ and young adults’ lives (Hallam et al., 2014 ; Kotsou et al., 2011 ), but few studies have differentiated the roles that emotional competence play in academic performance between adolescence (high school students) and young adulthood (college students). Therefore, more research is needed to address the role that emotional competence plays during the COVID-19 pandemic from a developmental perspective.

The current study

Above all, online learning readiness and emotional competence are critical for understanding adolescents’ academic performance during COVID-19. Given the lack of research on high school students’ online learning readiness and students’ emotional competence in online settings, little is known about whether online learning readiness and emotional competence may influence students’ academic performance differently for high school students (adolescents) and college students (young adults). Therefore, this study aimed to (a) investigate how online learning readiness and emotional competence contribute to students’ academic performance in both high school and college students during COVID-19 and (b) explore whether the pattern of associations would be different in high school students and college students. As mentioned above, college students with better online learning readiness have been shown to have higher online academic performance (e.g., Tsai & Lin, 2004 ; Yilmaz, 2017 ), and in a traditional face-to-face setting, students with higher emotional competence have tended to have better academic performance (e.g., Brackett et al., 2012 ; Harley et al., 2019 ). In aim (a), this study proposed two hypotheses: Hypothesis 1 —Both high school and college students with a higher level of online learning readiness will have better online academic performance during the COVID-19 pandemic; Hypothesis 2 —Both high school and college students with better emotional competence will have higher online academic performance during the COVID-19 pandemic. Without enough evidence in the extant literature for us to make a specific prediction, aim (b) will be examined in an exploratory manner.

Participants and procedure

High school sample.

This study recruited 1,689 first-year students from a high school in northeast China with medium education quality. As recommended by Kline ( 2015 ), the minimum sample-size-to-parameters ratio would be 10:1. In the high school sample, the number of model parameters that required statistical estimates was 99. The sample-size-to-parameters ratio in our study was 17:1, meeting the requirement of above 10:1. A survey was set up on Wen Juan Xing (a Chinese survey engine similar to Qualtrics). The head teacher first sent out the consent form to students’ parents through WeChat. Parents signed the form electronically and returned it to the head teacher. After obtaining consent from parents or guardians, the head teacher sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing “careless cases” (i.e., the responses from participants who failed the attention check), the final sample consisted of 1,316 first-year high school students (39.1% male, 53.8% female, and 7.1% preferred not to say). We incorporated two attention checking items to avoid careless responses. For example, for this question, please select disagree. Participants who answered both attention checking questions correctly were included in this study. Participants’ ages ranged from 15 to 18 years old ( Mean = 16.32, SD = 0.63); 94.2% identified their race as Han (i.e., the majority in China), and 5.8% identified as minorities.

College student sample

A sample of 1,049 college students was recruited from a 4-year university in northeast China with medium education quality. In the college sample, the number of model parameters that required statistical estimates was 75. The sample-size-to-parameters ratio was 14:1, above the recommended 10:1 (Kline, 2015 ). The same survey on Wen Juan Xing was used to collect data. A university lecturer first sent out the consent form to students or students’ parents or guardians through WeChat (with forms sent to parents/guardians only for those students who were under 18). After receiving the signed consent forms, the university lecturer sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing careless cases (i.e., the responses from participants who failed the attention check), the final sample consisted of 668 college students (43.3% male, 51.8% female, and 4.9% preferred not to say). Participants’ ages ranged from 17 to 25 years old ( Mean = 20.20, SD = 1.43). Among them, 149 were freshmen, 207 were sophomores, 76 were juniors, and 236 were seniors; 89.2% identified their race as Han (i.e., the majority in China), and 10.8% identified as minorities.

Measurement

Translation.

All questionnaires originally in English (i.e., questionnaires on emotional competence and online learning readiness) were translated into Chinese through translation and back-translation procedures (Beaton et al., 2000 ). Specifically, one Chinese postdoctoral student fluent in English translated the scales to Chinese, and another Chinese university lecturer back-translated all scales to ensure translation accuracy. A bilingual US university faculty member checked both the translated and back-translated scales to further validate the translation. The whole survey included demographic information (e.g., gender, age, race) and questionnaires on emotional competence and online learning readiness.

Emotional competence

Emotional competence was measured by the Short Profile of Emotional Competence (S-PEC), which demonstrated high internal reliability in the original study ( D-G Rho = 0.85; Mikolajczak et al., 2014 ). The S-PEC included five parallel subfactors in both the intrapersonal (10 items) and interpersonal (10 items) dimensions. Each of the five subfactors was assessed by two items. These subfactors were identification (e.g., “When I am touched by something, I immediately know what I feel”), comprehension (e.g., “I do not always understand why I respond in the way I do”), expression (e.g., “I find it difficult to explain my feelings to others even if I want to”), regulation (e.g., “When I am angry, I find it easy to calm myself down”), and utilization (“If I wanted, I could easily make someone feel uneasy”). All items were rated on a scale from 1 = never to 5 = very often . In our study, two items in each subfactor were averaged to create a composite score; a higher value indicated better emotional competence in that specific subfactor. In our samples, both the reliability (Cronbach’s α = 0.71 in the high school sample and 0.76 in the college sample) and the constructive validity (high school sample: χ 2 (25) = 48.12, p = 0.004, CFI = 0.99, TLI = 0.98, RMSEA (90% CI) = 0.03 (0.02–0.04), SRMR = 0.02; college sample: χ 2 (29) = 59.62, p = 0.001, CFI = 0.98, TLI = 0.97, RMSEA (90% CI) = 0.04 (0.03–0.05), SRMR = 0.03) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further validate the psychometric properties of this translated instrument, we conducted item response theory analyses, like Alavi et al. ( 2021 ) and Khodi et al. ( 2021 ). Specifically, we applied the polytomous Rasch Rating Scale model (Andrich, 1978 ) to both the high school and college samples. Rasch measurement theory provides a clear and theoretically based framework that allows researchers to evaluate the degree to which the instrument adheres to invariant measurement (Martha et al., 2021 ; Wind et al., 2021 ; Wind & Guo, 2019 ). We used Winsteps software (Linacre, 2016 ) to obtain model-data fit statistics (i.e., infit and outfit MSE ) and the reliability of separation statistics ( Rel ) for students and items. On average, the values of model-data fit statistics were around 1 for both high school students ( M infit MSE = 1.01, SD = 0.73; M outfit MSE = 1.02, SD = 0.72) and college students ( M infit MSE = 1.02, SD = 0.88; M outfit MSE = 1.00, SD = 0.84), and for items, the infit and outfit MSE were also close to 1 (high school sample: M infit MSE = 1.03, SD = 0.28; M outfit MSE = 1.02, SD = 0.26; college sample: M infit MSE = 1.00, SD = 0.27; M outfit MSE = 1.00, SD = 0.26), indicating acceptable fit to the Rasch model. The reliability of the separation statistic for students (high school sample: Rel = 0.86; college sample: Rel = 0.88) suggests that the instrument effectively differentiated students with different levels of emotional competence. Similarly, the reliability of the separation statistic for items (high school sample: Rel = 1.00; college sample: Rel = 1.00) indicates that there were differences in difficulty to endorse each item. We also conducted differential item functioning (DIF) analysis to determine whether the item response differed between high school students and college students while controlling for an estimate of emotional competence. Several researchers (Draba, 1977 ; Wind & Guo, 2019 ; Wright et al., 1976 ) have recommended that absolute logit differences that exceed 0.5 suggest that DIF occurs between two groups. Our results show that the range of differences in Rasch calibrations were from -0.38 logits to 0.43 logits, which indicates that there were no substantively meaningful differences between high school students and college students. In summary, the emotional competence instrument demonstrated acceptable psychometric properties for measuring emotional competence among both high school and college students.

Online learning readiness

Items that directly targeted the online learning environment on the Online Learning Readiness Scale (OLRS; Hung et al., 2010 ) were employed to measure online learning readiness. Specifically, there were three items in each of the following three subscales: computer/Internet self-efficacy (e.g., “I feel confident in my knowledge and skills of how to manage software for online learning,” Cronbach’s α = 0.74), learner control in online contexts (e.g., “I can direct my own learning progress in online courses,” Cronbach’s α = 0.73), and online communication self-efficacy (e.g., “I feel confident in expressing myself [emotions and humor] through text,” Cronbach’s α = 0.87). All items were rated from 1 = strongly disagree to 5 = strongly agree . Three items on each of the subscales were averaged to create a composite score so that a higher value indicated higher levels of online learning readiness on that subscale. In our samples, both reliability (Cronbach’s α ranged from 0.72 to 0.73 in the high school sample and 0.75 to 0.82 in the college sample) and constructive validity (high school sample: χ 2 (19) = 100.04, p < 0.001, CFI = 0.98, TLI = 0.96, RMSEA (90% CI) = 0.06 (0.05–0.07), SRMR = 0.02; college sample: χ 2 (22) = 45.70, p = 0.002, CFI = 0.99, TLI = 0.99, RMSEA (90% CI) = 0.04 (0.02–0.06), SRMR = 0.02) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further evaluate the psychometric properties of the translated OLRS, we also conducted Rasch analysis as we did for S-PEC. The results indicate that OLRS exhibited acceptable psychometric properties for measuring both high school and college students’ online learning readiness. Specifically, the average values of model-data fit statistics were around 1 for both groups (high school sample: M infit MSE = 1.00, SD = 0.98, M outfit MSE = 1.00, SD = 0.97; college sample: M infit MSE = 0.96, SD = 1.15; M outfit MSE = 0.97, SD = 1.17) and items (high school sample: M infit MSE = 1.00, SD = 0.19, M outfit MSE = 1.00, SD = 0.20; college sample: M infit MSE = 0.99, SD = 0.19, M outfit MSE = 0.97, SD = 0.20). The reliability of separation statistics for students (high school sample: Rel = 0.86; college sample: Rel = 0.87) and for items (high school sample: Rel = 0.99; college sample: Rel = 0.98) suggest that OLRS can effectively differentiate among individuals with different levels of online learning readiness. DIF analysis demonstrated that there were no substantively meaningful differences between high school students and college students (-0.41 ≤ logit difference ≤ 0.33).

Academic performance

After getting approval from their institutions, consent from students and their parents/guardians (for minor-aged students), we obtained students’ academic performance (indicated by final exam scores) from their teachers in both the high school and the college samples. In the high school sample, we collected students’ final exam scores on Chinese, math, and English—three major disciplines in the Chinese high school education system (the maximum possible score for each discipline was 150). In the college sample, we gathered students’ average final exam scores across all courses they had taken (the maximum possible score was 100). We collected participants’ scores at two time points (T1 and T2) for both samples. T1 was before the COVID-19 pandemic when traditional face-to-face teaching was used, and T2 was during the COVID-19 pandemic when online synchronous teaching was used. Students in both samples had similar online learning experiences. Specifically, the online synchronous teaching adopted Dingding (a Chinese meeting software application like Zoom), and Microsoft Office programs were used for assignments. WeChat (a Chinese messaging app) was utilized for teacher–teacher, teacher–student, student–student, and teacher–parent communication. For the high school sample, data were collected in December 2019 (T1) and July 2020 (T2); for the college sample, data were collected in January 2020 (T1) and June 2020 (T2). Students were assigned a four-digit research ID to confidentially link their final exam scores and the survey results.

Plan of analysis

Data analysis was conducted in Mplus version 8.4 (Muthén & Muthén, 2017 ). In both the high school and college samples, measurement models via confirmatory factor analysis (CFA) were first estimated on the latent constructs of emotional competence, online learning readiness, and academic performance (high school sample only), individually. Specifically, the latent variable of emotional competence was indicated by 10 composite scores—identification, comprehension, expression, regulation, and utilization in both intrapersonal and interpersonal domains. The latent variable of online learning readiness was indicated by three composite scores of computer/Internet self-efficacy, learner control in online contexts, and online communication self-efficacy.

In the high school sample, the latent variable of pre-COVID academic performance was indicated by students’ final exam scores on Chinese, English, and math at T1, and the latent variable of during-COVID academic performance was indicated by these three scores at T2. An overall measurement model including both the T1 and T2 latent constructs of academic performance was conducted after a CFA for each time point. In the college sample, because there was only a single score for each time point, that single score was used as a manifest variable for academic performance at T1 and T2. In each measurement model, correlations between residual variances were added one at a time according to modification indices (Sorbom, 1989 ).

Next, we used structural regression models to examine the association between emotional competence, online learning readiness, and students’ during-COVID academic performance while controlling for their pre-COVID academic performance and the demographic characteristics of age and gender. That is, the T2 academic performance variable was regressed on age, gender, T1 academic performance, emotional competence, and online learning readiness. All predictors were allowed to correlate with each other. This analysis was conducted separately in the high school and college samples.

Both measurement models and structural regression models were estimated using full information maximum likelihood estimation to minimize the bias caused by missingness (Widaman, 2006 ). Overall model fit acceptability was evaluated using the following criteria: the comparative fit index (CFI) value was greater than 0.95, the Tucker-Lewis index (TLI) was greater than 0.90, the root mean square error of approximation (RMSEA) was less than 0.06, and the standardized root mean square residual (SRMR) value was less than 0.08 (Hu & Bentler, 1999 ). Standardized path coefficients were reported in each model.

Last, group invariance tests were conducted across gender groups in both the high school and college samples to indicate whether the overall structural regression model was significantly different by gender. This was done by comparing two multiple group models that did not include gender as a control variable: in the first model, all regression paths were freely estimated across groups; in the second model, all regression paths were constrained to be the same across groups. Changes in the CFI (ΔCFI) were used as a preferred approach for model fit comparison, with ΔCFI equal to or greater than 0.01 indicating a significant change in model fit caused by path constraints (Cheung & Rensvold, 2002 ). This approach was more suitable than the Chi-square difference test for a large sample.

Table ​ Table1 1 includes descriptive statistics and correlation information for the variables used in the structural regression models. The lower panel shows correlations in the high school sample, and the upper panel depicts correlations in the college sample. Overall, variables were correlated in the expected directions in both samples. Moreover, the CFA models for emotional competence, online learning readiness, and academic performance across T1 and T2 all showed a good fit with the data (see Table ​ Table2 2 ).

Correlations, Means, and Standard Deviations

Note . Statistically significant correlations are bold and underlined ( p < .05). For gender: 1=male, 2=female.

The lower panel presents correlations in the high school sample and the upper panel presents correlations in the college sample.

EC=Emotional Competence, intra=intrapersonal dimension, inter=interpersonal dimension, id=identification, co=comprehension, re=regulation, ut=utilization; OL=Online Learning, eff=computer/internet self-efficacy, con=learner control in online contexts, com=online communication self-efficacy; T1 score=pre-COVID final exam score, T2 score=during-COVID final exam score; H.=high school sample, C.=college sample.

Due to space limit, high school T1 score and T2 score were composite scores (i.e. average score of Chinese, English, and Math at T1 and T2) in this correlation table (but they were latent variables in the formal analyses).

Model fit information for measurement models and structural regression models

Note . * p < .05, ** p < .01.

M1-M5=measurement model 1-5, S=structural regression model, EC=Emotional Competence, OL=Online Learning Readiness, T1 score=Pre-COVID Academic Performance, T2 score=During-COVID Academic Performance.

Model fit information of M2-M4 in high school sample and M2 in college sample were not available, since there were only 3 manifest variables loading on 1 latent variable in each model and these models were just identified

In the high school sample, the structural regression model had acceptable model fit, where χ 2 (153) = 669.25, p < 0.01; CFI = 0.94; TLI = 0.92; RMSEA = 0.05 (90%: 0.05–0.06); SRMR = 0.05. All regression paths are listed in Fig. ​ Fig.1 1 (a). Both emotional competence ( β = 0.06, p = .030) and online learning readiness ( β = 0.07, p = .006) were significantly associated with high school students’ during-COVID academic performance, even after accounting for the stability of their academic performance from the pre-COVID to during-COVID periods ( β = 0.78, p < .001) and controlling for the influence of age ( β = - 0.02, p = .489) and gender ( β = 0.07, p = .009).

The Associations of Emotional Competence, Online Learning Readiness, and Academic Performance. All predictors were correlated with each other. Residuals were allowed to correlated according to modification indices

In the college sample, the structural regression model had good model fit, where χ 2 (95) = 192.80, p < 0.01; CFI = 0.97; TLI = 0.96; RMSEA = 0.04 (90%: 0.03–0.05); SRMR = 0.04. All regression paths are listed in Fig. ​ Fig.1 1 (b). Only online learning readiness ( β = 0.15, p = .003) was significantly associated with college students’ during-COVID academic performance after accounting for the stability of their academic performance from the pre-COVID to during-COVID period ( β = 0.61, p < .001) and controlling for the influence of age ( β = 0.07, p = .061) and gender ( β = 0.08, p = .024). However, unlike the high school group, the association between emotional competence and during-COVID academic performance was not significant for college students ( β = - 0.02, p = .756).

Overall, the pattern of associations among variables was consistent across gender groups in both the high school and college samples, which was indicated by the insignificant change in the overall model fit (high school sample: ΔCFI = .000; college sample: ΔCFI = .002) between the model with constrained regression paths (i.e., constrained model) and the model with freely estimated regression paths (i.e., freely estimated model) across gender groups. This suggests that the association among emotional competence, online learning readiness, and during-COVID academic performance was representative of the whole sample (in the high school sample and college sample) regardless of a participant’s gender.

The present study was designed to evaluate how online learning readiness and emotional competence are related to students’ online academic performance during the COVID-19 pandemic. The results of structural regression models in both the high school and college samples generally supported our hypotheses. Consistent with Hypothesis 1, online learning readiness was associated with academic performance significantly for both high school students and college students (after controlling for their pre-COVID academic performance). However, there were some nuanced differences in the association between emotional competence and academic performance in the two samples. Partially consistent with Hypothesis 2, emotional competence was significantly associated with high school students’ academic performance, but such an association was not significant for college students. This finding also shed light on our second exploratory research question about the potentially different patterns of association among these constructs during adolescence (high school sample) and young adulthood (college sample). The association between online learning readiness and online academic performance was consistent across the two samples, but the association between emotional competence and online academic performance during COVID-19 was different.

The findings for online learning readiness were consistent with previous research (e.g., Cigdem & Ozturk, 2016 ; Horzum et al., 2015 ) and highlighted the vital role of online learning readiness in the high school population. Both high school students and college students who are more ready to learn online had better online learning academic performance. Specifically, high school and college students who have confidence in using Microsoft Office programs, managing software, and using the search engines (e.g., Google and Yahoo) were more likely to have higher academic performance (Tsai & Lin, 2004 ). Moreover, as in previous studies (Roper, 2007 ; Yilmaz, 2017 ), students who could direct their own learning online, avoid online distractions (e.g., instant messages or surfing the Internet), and communicate effectively with peers or instructors online demonstrated stronger academic performance during COVID-19.

All these findings are in line with classical developmental psychology theories, especially Bandura’s ( 1969 , 1977 ) interactive triangle of personal factors, personal behaviors, and environmental factors and Vygotsky’s ( 1978 ) social learning theory. A change in social and learning environment could influence students’ learning significantly, and how well students’ responses fit the environment are key factors of the learning outcome. Online learning and the pandemic are foreign for both high school and college students; the more ready students are, or the more quickly they can adjust to the new environment, the better their learning outcomes will be (Tu, 2002 ).

Developmental differences were identified in the associations between emotional competence and academic performance. The association between emotional competence and during-COVID-19 academic performance in the high school sample confirmed the findings from previous research that high emotional competence could contribute to academic performance (Brackett et al., 2012 ; Garner, 2010 ). Adolescents who could identify, comprehend, regulate, and utilize their own or others’ emotions performed better academically (Brackett et al., 2012 ; Durlak et al., 2011 ; Zins et al., 2007 ). Such findings are consistent with Pekrun’s ( 2000 , 2006 ) control-value theory of achievement emotion, which highlights the emotional arousal in academic settings elicited by academic achievement. Achievement emotion can influence cognitive, motivational, and regulatory processes associated with learning and achievement. Conversely, negative emotions consume energy that is essential for cognition and impair academic performance (Meinhardt & Pekrun, 2003 ). Therefore, adolescents who could better identify and regulate emotion achieved higher grades in the current study.

However, in the college sample, no association was identified between emotional competence and academic performance. This discrepancy in the association pattern between emotional competence and during-COVID-19 online academic performance is likely due to two factors: developmental differences and different measurements of academic performance. Developmentally, adolescents may have a harder time regulating emotions due to brain, body, and social relationship changes (Casey et al., 2019 ; Miller-Slough & Dunsmore, 2016 ), so emotional competence appears to be more critical for adolescents than young adults. The discrepancy might also be caused partially by the different measures of GPA (i.e., high school—Chinese, math, and English total grade; college—a single average score).

The current study has both theoretical and practical implications. The relatively large pooled sample sizes (15–25 years of age) enabled us to make more generalizable statistical inferences about both high school students (adolescents) and college students (young adults), at least in the Chinese student population. Theoretically, this study added to the limited literature on adolescents’ online learning readiness (Tsai & Lin, 2004 ) and replicated prior work in the college population to emphasize the important role online learning readiness plays in online academic performance during young adulthood (e.g., Hung et al., 2010 ; Rafique et al., 2021 ). Moreover, our findings extended previous research on the impact of emotional competence on psychological development outcomes (e.g., Kotsou et al., 2011 ; Valiente et al., 2020 ) to highlight its crucial role in online academic performance, especially for high school students.

Practically, this study informed both high schools and higher education institutions that preparing students to learn online is as essential as preparing the institution to operate online (Habibu et al., 2012 ; Littlejohn & Pegler, 2007 ). Being ready to transition to an online learning environment and having high emotional competence could make adolescents more resilient to COVID-19-related challenges, such as social isolation and learning loss (Shanahan et al., 2020 ). Educational institutions not only need to provide instructions on how to use Microsoft Office software and online searching techniques but should also provide learning strategies like how to avoid online distractions (e.g., social media and video games) and how to communicate effectively with teachers and peers online. Such guidance would be especially beneficial for students who think they are not ready for online learning. Moreover, students’ mental health issues need to be addressed by emotional competence-related interventions, especially for adolescents (Lau & Wu, 2012 ). Schools and universities should consider having interventions and training on emotional competence to promote students’ mental health and help them navigate the volatile, uncertain, complex, and ambiguous world (Hadar et al., 2020 ). Effective strategies of identifying, comprehending, regulating, and utilizing emotions should be offered via online instructions and activities, especially for high school students. Moreover, online counseling should be more accessible for adolescents (O’Connor, 2020 ; Wen et al., 2020 ).

Limitations

This study has some limitations that should be considered when interpreting its results. First, although pre-COVID academic performance has been controlled for from a longitudinal perspective, the directionality of the association between online learning readiness, emotional competence, and online academic performance during the COVID-19 pandemic could not be deduced due to the cross-sectional nature of the current data. The different measures of grade point average across the sample may have contributed to different findings for the groups Second, self-reported data on emotional competence and online learning readiness unavoidably introduced bias into the measurements. Thirdly, this study did not account for demographic control variables such as socioeconomic status, which can be a key factor contributing to students’ access to computers and the Internet or other resources. Moreover, the data collection intervals were different for high school and college students, being 2 months less for the latter group.

Future directions

Future studies that include students in small towns and rural areas will enrich the generalizability of our findings because our samples were predominantly students from cities. Rural or suburban students would likely have less access to online resources or learning resources in general (Lai & Widmar, 2021 ). Moreover, longitudinal research is needed to infer the associational patterns of emotional competence and online learning readiness with academic performance, considering the enduring and emerging nature of emotional competence during adolescence (including young adulthood) and their potential nuanced implications for academic performance trajectory. Regardless, this is one of the first studies, to our knowledge, that simultaneously considered cognitive and emotional factors associated with online academic performance across different developmental stages in adolescence during the COVID-19 pandemic.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Not applicable.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Beijing Normal University and Dalian Neusoft University of Information. We are in compliance with the 1964 Declaration of Helsinki and its later addenda.

Informed consent was obtained from all individual participants included in the study. For participants under 18 years old, parent and guardian consent were obtained.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Title: online estimation via offline estimation: an information-theoretic framework.

Abstract: $ $The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adaptively chosen covariates ("online estimation"). Motivated by connections between estimation and interactive decision making, we ask: is it possible to convert offline estimation algorithms into online estimation algorithms in a black-box fashion? We investigate this question from an information-theoretic perspective by introducing a new framework, Oracle-Efficient Online Estimation (OEOE), where the learner can only interact with the data stream indirectly through a sequence of offline estimators produced by a black-box algorithm operating on the stream. Our main results settle the statistical and computational complexity of online estimation in this framework. $\bullet$ Statistical complexity. We show that information-theoretically, there exist algorithms that achieve near-optimal online estimation error via black-box offline estimation oracles, and give a nearly-tight characterization for minimax rates in the OEOE framework. $\bullet$ Computational complexity. We show that the guarantees above cannot be achieved in a computationally efficient fashion in general, but give a refined characterization for the special case of conditional density estimation: computationally efficient online estimation via black-box offline estimation is possible whenever it is possible via unrestricted algorithms. Finally, we apply our results to give offline oracle-efficient algorithms for interactive decision making.

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Physiology education in China: the current situation and changes over the past 3 decades

• Xuhong Wei 1   na1 ,
• Ting Xu 1   na1 ,
• Ruixian Guo 1 ,
• Zhi Tan 1 &
• Wenjun Xin 1  

BMC Medical Education volume  24 , Article number:  408 ( 2024 ) Cite this article

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As an experimental biological science, physiology has been taught as an integral component of medical curricula for a long time in China. The teaching effectiveness of physiology courses will directly affect students' learning of other medical disciplines. The purpose of this study is to investigate the current situation and changes in physiology teaching over 30 years in Chinese medical schools.

National survey was conducted online on the platform SoJump via WeChat and the web. The head of the physiology department in medical school was asked to indicate the information of physiology education from three periods: 1991–2000, 2001–2010, and 2011–2020. The responses of 80 leaders of the Department of Physiology from mainland Chinese medical schools were included in the study for analysis.

The survey showed that the class hours, both of theory and practice, had been decreased. During the past 20 years, the total number of physiology teachers, the number of physiology teachers who had been educated in medical schools, and the number of technicians had been reduced, whereas teachers with doctor’s degrees had been increased. In addition to traditional didactic teaching, new teaching approaches, including problem-based learning/case-based learning/team-based learning, integrated curriculum and formative evaluation systems, had been employed, mostly for more than 5 years, in some medical schools.

The present study has provided historical data regarding the current status of physiology education in China and that in the past thirty years by showing that physiology education in China has developed quickly,even it faces many challenges.

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Physiology is an important foundational discipline in medical schools [ 1 ]. It studies how different cells, tissues and organs work together to maintain the normal function of the human body. The task of medical students is to learn how to diagnose and treat diseases. Therefore, it is necessary to first understand the functions of the normal human body by studying physiology, laying the foundation for subsequent courses, such as pharmacology, pathophysiology and clinical disciplines.

In 1998, the “Education Promotion Plan for the 21st Century” was published by the Ministry of Education of China. Since then, a massive increase in medical student enrollment has occurred [ 2 ]. Additionally, in 1998, stand-alone medical schools from the former Soviet model were merged into comprehensive universities in China to follow the model of medical education in the United States and other countries [ 2 , 3 ]. These changes accordingly raised new challenges to medical education, for example, a rapid increase in the number of students without sufficient teachers and a lack of effective teaching strategies and methods. Thus, a survey on the current situation and changes in physiology education, including course hours and teaching staff, is necessary.

The traditional teaching model in physiology courses relies heavily on teacher-centered didactic lectures, with the students being given approximately 90 min of theoretical knowledge in the classroom in their second year of study. There were also a number of laboratory practices that ran concurrently with or subsequent to the lectures. Lecture-based learning (LBL) is good at transferring massive knowledge, the foundational cognitive skill from information professionals to students, but is often limited in facilitating the development of Bloom’s higher-order cognitive skills in students [ 4 ] due to passive acceptance of knowledge. Instructional strategies, such as problem-based learning (PBL), case-based learning (CBL) and team-based learning (TBL), which can promote active learning, have been widely adopted in medical education [ 5 , 6 , 7 , 8 , 9 , 10 ]. Their common merits involve developing cooperation among students, arousing consciousness of lifelong learning and improving problem-solving skills. In 2016, the "Chinese Undergraduate Medical Education Standards—Clinical Medicine Major" was released by the Ministry of Education, which aimed to develop student-centered and self-directed learning as the main content of educational strategies. This pointed out the direction for improving the level of medical education in China. Thereafter, an increasing number of student-centered learning methods, including PBL, CBL and TBL, are gradually being integrated into Chinese medical education. For example, problem-based self-designed experiments in physiology laboratory teaching are currently being adopted in Zhejiang University School of Medicine [ 11 ]. However, a national survey of the current PBL/CBL/TBL application status in physiology is still lacking.

In the 1950s, Case Western Reserve University first implemented an organ-system-based curriculum. In 1993, the curriculum reform of the Edinburgh World Medical Education Summit and the National Outstanding Doctor Training Plan opened the prelude to curriculum integration teaching in domestic medical colleges. In 1994, Reagan and Menninger reported on 10 years of experience by integrating physiology with other basic biomedical disciplines, such as anatomy, biochemistry, and pharmacology, in a PBL format [ 12 ]. In 2002, Shantou University Medical Schools first adopted an integrated curriculum in China [ 13 ]. In 2013, an integrated medical curriculum between basic medical courses and clinical curriculum was required in “several opinions of the ministry of health on implementing comprehensive reform of clinical medical education” [ 14 ]. Since then, the curriculum integration teaching model based on the concept of medical integrity and centered on the Organ system has become the new teaching reform. In 2020, the State Council General Office also stated “accelerating the innovative development of medical education and promote classroom reform in medical education by applying modern information technology in medical education” in China [ 15 ]. Under the new situation, reform in medical education has been accelerated, and the integration of modern information technologies in physiology teaching has been promoted.

In China, 11 broad categories, such as basic medicine, clinical medicine, stomatology, public health and preventive medicine, traditional Chinese medicine, were included in medical education. Clinical medicine is the main body of the medical education system in China, with 192 medical schools providing clinical medicine education [ 16 , 17 ]. As a particularly important basic medicine, it is taught as a discipline-based curriculum that emphasizes one-sidedness but lacks the overall concept of medicine in most medical schools,. Integrated teaching can integrate physiology with other disciplines, such as anatomy, pharmacology or clinical curricula, in a unified manner, thereby strengthening students' cognition of disease from different dimensions and levels, which is beneficial for broadening students' vision and reducing repetitive and unnecessary teaching content. In the 1950s, an organ-system-based integrated curriculum was first implemented at Case Western Reserve University. The integrated curriculum of medical education in China began in the 1990s [ 18 , 19 ]. In 2014, the "Deepening the cultivation of clinical medical talents through clinical practice and medical education collaboration" was issued by the Chinese Ministry of Education (MOE) [ 20 ]. This might greatly accelerate the reform of integrated medical courses.

A recent published study by Feng et al. has evaluated changes in Chinese medical schools for Physiology teaching over the last 20 years [ 21 ]. For the better development of physiology curricula, in the present study, we conducted a survey on physiology teaching in China to understand the current state and changes in the past 30 years, including course hours in theory and practice, faculty compositions, practice type and conducting time, and teaching approaches. Different to Feng’s study, in the present study we payed more attention to the changes of experiments type and the new teaching approaches. In addition to achieving the similar results as Feng’s study [ 21 ], we found that the total number of teachers in the physiology department had gradually decreased in the past 30 years, which was different from Feng’s study showing that the total number of physiology teachers was reported unchanged in most schools. We also found that the explorative and virtual experiments have developed quickly, which has not been reported previously. Moreover, our results also showed different integration content.

Study design

The main purpose of the study was to understand the current situation and changes in physiology education and teaching in the Chinese mainland, focusing on course hours, faculty compositions, practice type and conducting time, and teaching approaches. The changes in physiology teaching, particular the decline rate in course hours in the past 30 years, was the main outcome measures. Therefore, we had estimated the the decline rate by consulting literature in advance. A line of previous study has shown that a total of 83.33% of the surveyed schools have reduced their Histology and Embryology Education, which is also an important course in basic medicine in China [ 22 ]. We estimated the contact hour of physiology was reduced similarly. According to the formula Z 2 1 -ɑ/2 *pq/d 2 , in which Z 1-ɑ/2  = 1.96, p  = 83.33%, q = 1–83.33% = 16.77%, d = 0.1* p  = 8.33%, the estimated sample size was 79, which meant that we need to include decline rate in physiology course hours from at least 79 medical schools to achieve effectiveness. Accordingly, we conducted a nationwide survey of the top 100 medical schools (according to Evaluation Metrics (STEM) and 5- year total STEM [accumulative STEM (ASTEM)]  http://top100.imicams.ac.cn/ASTEM/college ), including different levels of ranked universities in China, including Project 985, Project 211 schools, and ordinary universities. All of them have had a five-year clinical medicine programs for at least ten years, as the present study investigated only five-year clinical medicine programs, which are the most popular medical program in China. Therefore, we thought the top 100 schools could represent the whole China and could meet the research needs. The present study was conducted on line from November 2020 to June 2021. Under the approval of the college research and ethics committee, a cross-sectional study was conducted among the directors of the physiology departments of the top 100 medical schools, excluding those Hong Kong, Taiwan and Macau, and these medical institutions that are distributed in various provinces. Traditional Chinese medical schools, specialist technology colleges, and medical schools without five-years medical programs were also excluded.

Eligibility criteria for participants

Inclusion criteria for choosing participants for this study involved: (1) participants should be directors of the physiology departments, that usually had extensive practical experience in teaching and had experienced or witnessed changes in theoretical and laboratory teaching reform in physiology over the past 30 years. (2) participants should be from the top 100 medical schools, distributed throughout almost every provincial-level administrative division in mainland China. (3) The participants had completed their PhD or MD degrees and had taught physiology in medical schools for at least one year. (4) Both male and female could be included.

Data collection

Quantitative data were generated from a self-administered survey questionnaire. The questions was first generated from the perspective of front line physiology teachers, who had extensive practical experience in teaching and had experienced or witnessed changes in theoretical and laboratory teaching reform in physiology over the past 30 years. Then the questionnaire was designed based on the published literature, discussed by the authors and tested by teachers from the corresponding author’s school and was subsequently revised based on the feedback to ensure clarity of the questions. Therefore, the description of each question was easy to understand and was structured elaborately, and more importantly, it is suitable to evaluate the teachers’ perspectives regarding the current situation and changes over the past 3 decades in physiology education in China. The questionnaire contained 26 main questions, 4 of which were jump questions. The questionnaire was designed based on the published literature (22). The survey was developed to collect factual information covering three main areas of physiology education: (1) the changes in course hours, including theory and practice, during the past 30 years. There were 11 questions. All the questions were filling in the blank except question 4. For example, question 2 was: What was the duration of physiological theory courses in clinical medicine at your university, from 2001 to 2010; (2) the changes in physiology teaching strategies and assessment. There were 15 questions. For example, question 12 was: The physiological experiment course in the clinical medicine major of your university is set as the following: (If the option includes exploratory experiments, please answer 12a. If the option does not include exploratory experiments, please choose no in 12a). There were 8 choices for question 12, including A. basic; B. comprehensive; C, explorative; D, basic and comprehensive; E, basic and explorative, F, comprehensive and explorative; G, basic, comprehensive and explorative practices; H, no. Question 12a was: What was topic selection method for exploratory experiments at your university? The choices for question 12 was: A, designed by students and tutored by teachers; B, designed by the students; C, designated by the teachers; D, No. (3) Changes in the teaching staff in physiology education. There were 10 questions. For example, question 19 was: What is the proportion of physiology teachers with doctoral degrees that are teaching clinical medicine currently at your university? The choices for question 19 was: A, ≤ 50%; B, 51%-70%; C, 71%-90%; D ≥ 90%. Hence there were a total of 37 questions in the whole questionnaire, including the last question requiring the participants to show their names and schools. The questionnaire is not a structured scale with similar scale anchors or values (The anchor ran from 1 = ‘Not at all’ to 5 = ‘To a very large extent’). There is weak correlation between questions and each question has different rating level. The directors of the physiology departments of these schools were in a messaging group in the WeChat application (Tencent Holdings Ltd., Shenzhen, China). The participants was first informed all about the study’s purpose, their right to withdraw at any time, and that their data would not be leaked. A two-dimensional code (SoJump, 2019, attached in Supplementary Material 1 ) invitation to participate in the online survey on the platform SoJump (Changsha Ranxing Information Technology Co Ltd., Changsha, China) was then sent to the WeChat group, a popular social media mobile application. Participation was voluntary and unrewarding. Respondents completed and submitted the questionnaire via mobile phone or computer, which has unique IP address, so that the authors could know whether one participants had submitted the answers twice with the same device. To increase their engagement and the authenticity of their answers, the participants were required to read the instructions before doing the survey. The contact information of the participants was also sought through personal contacts and websites. Most completed surveys were followed up with phone calls or email to confirm the accuracy of the information, to clarify obscure answers and to help the respondents complete omitted items if they were willing to do so. The results will not be adopted in statistics if the filling time was too short and the incomplete information could not be supplemented. To prevent the use of repeated responses from the same medical school, the respondents were required to show their institutions. The directors were also required to show their names to ensure that they submitted a single answer from each school. In addition, the answers could also give hints whether the participants had taken the survey seriously as some items from different questions confirm each other. For example, the number of participants that choose choice H in question 12, choice E in question 12a and choice G in question 13a are the same, which means the same content that the authors wanted to obtain was answered consistently by the participants, even the content was presented in different ways.

Finally, a total of 82 responses (from 51 female and 31 male participants) were finally identified as valid, however, 4 different participates from 2 schools were identified to have submitted the questionnaire simultaneously, and therefore only 80 medical schools have attended the survey. The surveyed schools were more than that in Xin Cheng’s study, which was 66 (22). Hence, the survey response rate was considered 80%. How the 82 directors from the 80 medical schools represent the overall total medical teachers are shown in Table  1 .

Statistical analysis

Statistical analysis questionnaires with missing items were considered ineffective and excluded from subsequent analysis. The data collected were tabulated in Microsoft Excel 2016. All statistical analyses were performed using GraphPad (Prism 8.0, San Diego, CA). One-way analysis of variance (ANOVA) (with the post hoc Tukey test) was performed to assess the physiological contact hours. For all tests, P  < 0.05 was considered significant. The results are expressed as the means ± SD. Effect size was shown by Cohen’s d value, which is determined by calculating the mean difference between two groups and then dividing the result by the pooled SD, that is, Cohen’s d = (Mean2-Mean1)/SD pooled, where SDpooled = √(SD1 2  + SD2 2 )∕2. To determine the internal consistency of the responses, Cronbach’s alpha test was used to analyze the data obtained from the questionnaires.

The geographical distribution of the surveyed medical schools

Finally, the 80 medical schools that had attended the survey were distributed in 29 provinces/municipalities. The geographical distribution of the surveyed medical schools is summarized in detail in Table 2 .

Changes in course hours in the physiology curriculum

This study focused on the current status and the changes in physiology education and teaching in China, however, some participants are not familar with early physiology teaching, making it challenging to get exact information. We have informed the participants that they could leave blank if they don't know the answer. At last we found that among the 80 medical schools, 76 participants supplied the exact number of their current physiology contact hours from 1991 to 2020. From Fig.  1 , we can see that compared to 1991–2000, the schools with class hours > 110 had been gradually reduced in 2001–2010, and had disappeared in 2011–2020. In contrast, the schools with class hours in the range of 51–70 had been continuously increased in the past 3 decades (Fig.  1 A-C). As shown in Fig.  1 D, the average contact hours of physiology were gradually decreased in the past 3 decade. in the 76 medical schools (mean ± SEM, 73.2 ± 1.4 for 2011–2020, 80 ± 1.4 for 2001–2010, 85.9 ± 2 for 1991–2000, F(2,225) = 15.51, P  < 0.0001).

Survey of various aspects of changes of physiology curriculum in Chinese medical schools in the past 3 decades. A - C The bar charts show the numbers of schools with each range of the total number of contact hours of physiology in the 3 period as indicated in the surveyed Chinese medical schools. D  Comparison of the average physiology (theory) contact hours in each academic year in the 3 periods. * P  < 0.05; ** P  < 0.01, **** P  < 0.0001 compared to the related group

Changes in physiology teachers

The same survey was also conduced to understand the changes in physiology teachers, who are the primary resource for educational development. From the survey, it was found that most directors of physiology departments who responded to the survey were experienced in physiology teaching. As shown in Fig.  2 A, among the 82 respondents, most of them have had a range of 26 to 35 years, even 10 had more than 36 years of teaching experience; only 1 had ≤ 5 years of experience. The present study also showed that in the majority of the medical schools, > 70% of the teachers had received their doctor’s degree (Fig.  2 B), demonstrating that the physiology teachers had good educational backgrounds. Furthermore, in 10, 29, 24 and 17 of the surveyed 80 medical schools, ≤ 50%, 51%-70%, 71%-90%, and ≥ 90% of the teachers had been educated in medical schools, respectively (Fig.  2 C). A massive increase in student enrollment in medical schools has occurred since 1998 in China. To understand whether there was a sufficient number of physiology teaching staff to ensure teaching quality in China, the appropriate number of teachers who had worked at the same time in the physiology department in the past 30 years was quantified. The results show the numbers of surveyed medical schools with different teacher numbers in the three periods of 1991–2000, 2001–2010, and 2011–2020. The results showed that the number of schools that had teachers ranging from 1–10 increased continuously, whereas the number of schools that had teachers ranging from 11–20 decreased continuously in the pat 3 decades (Fig.  2 D). Compared to 20 years ago, most of the surveyed schools had decreased numbers of teachers possessing medical doctor’s degrees in the physiology departments (Fig.  2 E).

Survey of various aspects of physiology teachers at the surveyed Chinese medical schools. A The numbers of directors of physiology with each range of physiology teaching experience. B-C , each percentage range of physiology teachers with doctor’s degree ( B ) and with medical educational backgrounds ( C ). D The changes in the percentages of the total number of physiology teachers within the 3 periods. E , F Changes of the numbers of physiology teachers with medical educational backgrounds ( E ) and the number of technician staff ( F ) over the past two decades

Technicians contribute greatly to physiology education by preparing the material and maintaining experimental instruments and related software. The survey showed, however, that the number of technicians, compared to that 20 years ago, decreased in 46.3% of Chinese medical schools and increased in only 30.5% of medical schools (Fig.  2 F), suggesting that there have not been enough technicians in recent years.

Changes in physiology practice

Experimentation is fundamental to scientific methods of physiology. A range of 31 to 60 course hours in physiology practice in clinical medicine major in China was predominant. The course hours in the range of 60–90 greatly decreased during the period of 2000–2009 compared to 1990–1999 (Fig.  3 A). The numbers of schools that having less than 30 students in each laboratory had decreased continuously from 1991 to 2000, whereas the numbers of schools that having 41–60 students in each laboratory were gradually increased since 1991 (Fig.  3 B). In each laboratory in the medical schools in China, usually only one teacher tutors all the students when they are doing the practice. Therefore, an increased number of students in each laboratory will lessen the amount of time that the teacher can communicate with each student. According to the survey results, regarding the types of practice, basic, comprehensive, basic and comprehensive, basic and explorative, comprehensive and explorative, and basic, comprehensive and explorative practices were conducted in 9%, 1.3%, 29.5%, 10.3%, 2.6%, and 47.4% of the surveyed schools, respectively (Fig.  3 C).

Survey of various aspects of physiology laboratory practice in Chinese medical schools. A- B The bar charts show the numbers of schools with each ratio of practice courses hours for physiology ( A ), with each range of the number of students in each laboratory ( B ). C , the setting types of practice in the surveyed Chinese medical schools are shown. D , E The pie charts show the percentage of schools that started explorative practice in the 3 period ( D ) or how the explorative practice topics was selected ( E ) compared to those 20 years ago in the surveyed Chinese medical schools. F The pie chart show the percentage of schools that employed physical experiments, virtual experiments, physical experiments combined with virtual experiments, physical experiments combined with watching videos, physical experiments combined with virtual experiments and watching videos, respectively. G  The bar charts show the numbers of schools with each range of virtual experiments conducting time. H  The bar charts show the numbers of schools with each virtual experiment sources

Regarding explorative practices, 28 schools had adopted them for less than 5 years, 19 for more than 5 years but less than 10 years, 12 for 11–15 years, and only 2 for more than 15 years. Sixteen schools had not yet adopted explorative practices (Fig.  3 D).

In 63.4% of the surveyed schools, the exploratory practice topic was designed by students and tutored by teachers. In 23.9% and 12.7% of the surveyed schools, it was designed by the students or by the teachers, respectively (Fig.  3 E).

It is difficult to control testing variables in physical experiments. Videos that showing experiments procedures and virtual experiments are becoming considerable options that have greatly altered physiology teaching. A total of 10.3%, 6.4%, 24.4%, 35.9% and 23.1% of the surveyed schools employed physical experiments, virtual experiments, physical experiments combined with virtual experiments, physical experiments combined with watching videos, physical experiments combined with virtual experiments and watching videos, respectively (Fig.  3 F). Virtual experiments had been employed for less than 5 years in 28 and 19 of the surveyed schools, whereas it had been employed for more than 10 years in only 14 schools. In 16 schools, virtual experiments have not yet been employed (Fig.  3 G). Furthermore, the virtual experiment sources were purchased from the company in most of the surveyed schools, secondly developed by the school and the company together. Only 2 schools developed the virtual experiments by themselves (Fig.  3 H).

Changes in physiology teaching approaches

Medical education in the West has undergone several influential reforms, such as the development of PBL at McMaster University in the 1960s [ 23 ] and an integrated curriculum at Newcastle University and Case Western Reserve University in the 1990s [ 18 , 19 ]. The survey was conducted to understand whether these reforms have also influenced physiology education. The results showed that in addition to traditional didactic teaching, teaching methods have also been innovated in some medical schools in China.

At the time survey, PBL, CBL or TBL had been implemented in 74.4% schools, integrated curriculum models had been tried in 68.2% medical school, and formative evaluation systems had been established in 75.1% schools (Fig.  4 A). In the schools that had tried PBL, CBL or TBL, 61.3% of them had experience of more than 5 years). 57.1% of the schools that had implemented integrated curriculum models for more than 5 years. In addition, 46.9% of the schools had tried formative evaluation systems for more than 5 years (Fig.  4 B).

Survey of various aspects of physiology teaching strategies and assessments in Chinese medical schools. A The bar charts show the percentages of the medical schools that have or have not implemented PBL/CBL/TBL, integrated curricula, and formative assessments. B  The percentages of the medical schools with each range of employing time of PBL, integrated curricula, and formative assessments. C The percentages of the medical schools that have employed PBL/CBL/TBL in all chapters, partial chapters or system-oriented local content of physiology. D The pie charts show the percentages regarding with which course physiology have been integrated with. E , How the contact hour of physiology changed after integration

Moreover, the survey also demonstrated that over half of the schools (55.7%) implemented the PBL/CBL/TBL curriculum in partial chapters of physiology textbooks. A total of 36.1% had implemented system-oriented local content, and only 8.1% had implemented it in all chapters of physiology textbooks (Fig.  4 C). A total of 63.1% of the schools that reported implementing integrated curricula also reported integration with clinical sciences, 21.1% with basic medical science, 10.5% reported integration theory with practice, and 5.3% reported integration with other curricula. At the time of the survey, curricular integration between theory and practice was reported in 10.5% of the surveyed schools. Integration with clinical sciences and other basic medical sciences was reported in 63.2% and 21.1% of the surveyed medical schools, respectively (Fig.  4 D). Furthermore, at least half of the schools that had conducted integrated curricula reported reduced contact hours in physiology. A total of 32.8% and 10.3% reported intact and growing contact hours after integration, respectively (Fig.  4 E).

Physiology education is a microcosm of the reform and development of the medical education in the Chinese mainland. Hoping to improve the quality of preclinical medical education, the present study was undertaken to present an overview of current status and the changes in physiology education, focusing on course hours, teaching strategies and student assessments, teaching staff in China by conducting a nationwide survey.

A total of 82 responses were finally included in the reports, representing 80 top medical schools. The survey focused on the teaching of clinical medicine students, which usually comprise the largest programs at medical schools. The respondents covered most of the top 100 Chinese medical universities/schools; therefore, the information collected by the survey could represent Chinese medical universities/schools. The results showed that the number of teaching hours spent on physiology at medical schools in China has been significantly reduced, in the past 30 years. In addition, both the quantity and composition of teachers have changed considerably. Traditional didactic teaching is still predominant even though new teaching approaches, including problem-based learning/case-based learning/task-based learning, integrated curriculum and formative evaluation systems have been conducted.

It is well known that small group teaching and exposure to practicals benefit learning, however, the survey showed that both the lecture contact time and laboratory practice hours of physiology in each academic year had decreased in the past 30 decades. Decreased course hours on physiology have occurred worldwide not only in recent times but also in earlier times, both in China and overseas. It has been reported that from 1955–56 to 1985–86, laboratory hours devoted to animal and human physiology declined by 92% [ 24 ]. Consistently, the recent study by Feng et al. also shows that the physiology class hours and the ratio of physiological theory to laboratory have been decreased over the last 20 years [ 21 ]. The reason that physiology class hours are decreased, however, is complicated. At present, an increasing number of students are using internet-based e-learning, such as watching videos. Thus, one important reason for decreased physiology hours is the construction and application of online open courses, which can enable students to learn everywhere at any time by removing temporal-spatial barriers. One other reason for reduced course hours is perhaps to save time for students to do scientific research and for clinic curriculum, which is catering to the demands of modern medical education. The third reason might also be the outcome of educational advancement, that is, the students had been taught some of the physiology knowledge at high schools or even middle schools and there is no need to repeat teaching these knowledge in universities.

Undoubtedly, a high ratio of qualified teachers to students is desirable for medical education. Unfortunately, the survey showed that the total number of teachers in the physiology department had gradually decreased in the past 30 years, in contrast to the rapidly expanding student enrollment [ 25 ]. The recent study by Feng [ 21 ], however, has shown that the total number of physiology teachers remains unchanged rather than decreased. The reason for the difference, however, is still not clear. In the present study, we surveyed the directors of physiology discipline, whereas Feng’s study surveyed the heads or senior teachers. The different survey subjects might affect the survey results.

For the teaching experience of the respondents, the data showed that 98.8% of the respondents had over 5 years of teaching experience and most of them have had a range of 26 to 35 years, even 10 had more than 36 years of teaching experience, demonstrating that the directors have rich experience. There is no doubt that rich teaching experience is good to education. From another perspective, however, this data also suggest that the directors have a relatively old age, and perhaps it is getting difficult for them to accept new teaching strategies. Moreover, consistent with Feng’s study [ 21 ], our results showed that teachers possessing doctor’s degrees has increased, whereas teachers with medical education backgrounds has decreased. There are perhaps two reasons that contribute to these phenomena. First, student enrollment has been greatly expanded in most Chinese medical schools, and it is becoming increasingly difficult for graduates to obtain appropriate jobs. To alleviate employment pressure and improve competitiveness, they must pursue a PhD career. Second, fewer students with medical education backgrounds are willing to pursue a career in the full-time teaching of basic medical sciences [ 2 ], possibly because the income gap has widened further. Third, universities are excessively emphasizing scientific research achievements when recruiting the teaching staff and having a doctoral degree is the most basic requirement for entering the university. The increasing number of teachers with PhD degrees has two sides to physiology teaching. On one side, it helps to cultivate students' scientific research thinking as the teachers have received well training in doing scientific research. On the other side, teachers are often overburdened by spending too much time doing their own scientific research, and therefore, the attention that can be paid to teaching undergraduates will be greatly decreased. Some teachers would even think that teaching is a waste of time, so that the universities have to consider that attending undergraduate courses for a certain amount of time as one of the basic evaluation indicators. The decreasing number of teachers with medical education backgrounds suggest that the physiology teachers have difficulties in connecting with clinical practice during teaching. To ensure the teaching quality, on the one hand, medical schools should help the physiology teachers to cultivate the transforming medical concepts by establishing combined teaching teams between basic and clinic. On the other hand, medical schools should take strategies to reduce the pressure of the teachers from scientific research, so that they are willing to spend more time in teaching.

Every understanding or conclusion of physiology is obtained from practice. Practice also assists students in applying physiology to clinical applications. Regarding explorative practices, 28 schools had adopted them for less than 5 years, 19 for more than 5 years but less than 10 years, 12 for 11–15 years, and only 2 for more than 15 years. 16 schools have not yet adopted explorative practices (Fig.  4 d).

Experimental courses are a very important part of physiology teaching. By conducting experimental courses, students' observation abilities and hands-on abilities can be better cultivated. However, some physiological experiments are time consuming or require expensive equipment. Furthermore, testing variables in physical experiments is difficult. Experiments on live animals also require high levels of biological security. The application of virtual experiments can effectively solve these difficulties by enabling students to experience the experimental process. The survey showed that virtual experiments are adopted by more and more schools, though most schools chose to purchase the virtual experiment sources by the companies. This results indicate that teaching strategies have been greatly impacted by the development of artificial intelligence. A recent study has introduced how to build an electronic standardized patient (ESP) based-virtual human body system powered by the real-time human physiological parameters in the teaching of human physiology. These ESP-based virtual simulation projects presumably becomes a considerable option for the first-class course construction in physiology [ 26 ]. In another study, the effectiveness of virtual labs in practicing biochemical experiments was assessed and the student's feedback regarding this tool was examined, showing that using virtual laboratories is effective in delivering practical parts of basic medical experiments to medical students and that the students have positive attitude toward using virtual laboratories in the practical sessions of a Medical Biochemistry course [ 27 ]. The authors believe that in the future, an increasing number of medical schools will employ combined virtual experiments and traditional experiments, as they can complement each other. Integrating medicine and industry will promote the progress of the medical education.

The high enrollments also raise the question that more students learn in the same class and share one teacher. With reduced teachers and lecture contact time hours, it is thus a challenge for departments to adequately organize teachers to maximize student learning and allow for student-centered teaching approaches. To compensate for the consequences of insufficient offline teaching, online teaching platforms that help to integrate and utilize teaching resources should be vigorously advanced. Currently, MOOCs education platforms such as the Chinese University MOOCs platform, XuetangX online platform, Zhihuishu MOOCs platform, and Chinese MOOC platform have been vigorously developed [ 21 ]. Microlectures are also popular in China. These online learning platforms are of great significance in cultivating students' professional knowledge and enhancing their innovative abilities. However, there are still issues that need to be improved, for example, teachers' online teaching ability needs to be improved, and students' autonomous interest in online learning needs to be stimulated.

For a long time, medical education, requires the medical students first learn basic and biomedical sciences and then move to clinical sciences. It emphasizes one-sideness but lacks the overall concept of medicine, has posed new challenges to the current medical training model and curriculum systems. In addition, the patients are presented in a totally different way as the traditional medical education. Integrated teaching can integrate different disciplines in a unified manner, thereby strengthening students' cognition of disease from different dimensions and levels, which is beneficial for broadening students' vision and reducing repetitive and unnecessary teaching content. It has been proven that the integrated teaching concept centered on organ systems benefits students’ early formation of medical concepts [ 28 , 29 ]. At the time of survey, the majority of the medical schools employed integrated curriculum models. At the time of the survey, integration between physiology and clinical sciences (vertical integration) was reported in more than half of the schools. Integration physiology between other basic medical sciences (horizontal integration) was employed fewer than vertical integration, whereas integration between theoretical lectures and practice sessions was least adopted. These results are consistent with a previous study showing that most curricula for medical education have been integrated horizontally and vertically. Most of the integration was integrated vertically between basic and clinical sciences, yet an vertical integration with humanism, and health population in the vertical axis, not only in the early years but also throughout the curriculum is also needed [ 30 ]. Furthermore, most of the schools with integration courses had decreased contact hours in physiology. These results are consistent with previous reports that in a vertically integrated curriculum the time spent on classroom education gradually reduces across the years, while the time on clinical practice increases [ 31 ]. These results also indicate that medical educators have realized that the old curriculum system is not conducive to cultivate systematic clinical thinking patterns, and have already taken steps towards reforms. Reduced classroom hours but enhanced graduation requirement indicated the teaching strategies must be improved. Furthermore, the teaching quality evaluation systems must also be improved. Problem-based learning is an approach that is often used with the aim of creating curricular integration. To improve teaching strategies, PBL, CBL or TBL was implemented in most of the surveyed schools, with more than 5 years of experience.In addition, 46.9% of the schools had tried formative evaluation systems for more than 5 years.

Limitation of this study

Not all the medical schools that provide five-year clinical medicine programs were included in the study, as the sample was not selected randomly.

Conclusions

The present study has provided historical data regarding the current status of physiology education in China and that in the past thirty years. Physiology is still mainly taught as a discipline-based curriculum in most medical schools, even though it is integrated with other disciplines. Physiology education in China faces many challenges, such as decreased course hours and decreased teachers with medical backgrounds. Although innovative teaching strategies have been employed in some medical schools, traditional didactic methods are still mostly used. Overall, the present study helps to understand the current status of physiology education in China and raises some concern for the better development of physiology education. Although the sample may not be truly representative of whole China, they were representative of the top 100 medical schools in the mainland of China.

Availability of data and materials

Data and materials can be obtained from the corresponding author upon request.

Abbreviations

Lecture-based learning

• Problem-based learning
• Case-based learning

Team-based learning

The Chinese Ministry of Education

Science and technology evaluation metrics

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Acknowledgements

The authors would like to thank all of the respondents who completed the survey and enabled this article to be written.

This study was supported by Teaching Reform Project from Guangdong Provincial "New Medical Science" Construction Guidance Committee (2023) and Teaching Quality Project of Sun Yat-sen University (500000–12220011).

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Xuhong Wei and Ting Xu contributed equally to this study.

Authors and Affiliations

Department of Physiology, Zhongshan School of Medicine, Science and Technology Building, Sun Yat-Sen University, East Wing, 74 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People’s Republic of China

Xuhong Wei, Ting Xu, Ruixian Guo, Zhi Tan & Wenjun Xin

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X.W. prepared the Figs 1 , 2 , 3 and 4 and Table 1  and 2   and wrote the manuscript. T.X. analyzed the questionnaire and critically revised the manuscript. R.G. tested the questionnaire and revised it. Z.T. and W.X. conceived the study and supervised it.

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Correspondence to Zhi Tan or Wenjun Xin .

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Wei, X., Xu, T., Guo, R. et al. Physiology education in China: the current situation and changes over the past 3 decades. BMC Med Educ 24 , 408 (2024). https://doi.org/10.1186/s12909-024-05395-1

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DOI : https://doi.org/10.1186/s12909-024-05395-1

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