The Focus and Trends of Digital Transformation in Higher Education: A Bibliometric Analysis Based on Web of Science 2014–2024

3.1.1 Author co-citation analysis

An analysis of the total number of citations by author shows that there are six scholars with more than 100 citations. Garrison is a well-known professor at the University of Calgary in Canada. He has long been committed to the research of educational technology and distance education and has created the Community of Inquiry (CoI) framework for studying text-based learning interactions in online learning environments. He has also proposed the importance of teachers’ sense of presence and teacher-student interaction in distance learning. Many researchers, such as Borup et al. [10] and Abou-Khalil et al. [11], have explored teachers’ instructional strategies and students’ learning strategies in online learning within Garrison’s theoretical framework.

Davis is a professor at Texas Tech University in the United States. He proposed the Technology Acceptance Model (TAM) in 1986, which emphasizes the key roles of perceived ease of use and perceived usefulness in technology adoption. TAM has been widely cited in discussions of higher education trends in the digital age, as seen in the works of Abdullah and Ward [12] and Cai et al. [13].

Hair is the dean of the Business School at the University of South Alabama, specializes in quantitative methods, including multivariate analysis and structural equation modeling. His work emphasizes rigorous measurement model validation, the flexible application of PLS-SEM, and multidimensional structural model evaluation, offering a standardized framework for empirical research. These principles have been widely adopted, as demonstrated in studies by Al-Adwan and Al-Debei [14] and Abbad [15]. Venkatesh is a professor at Virginia Tech in the United States. A key focus of his academic investigations lies in the domains of information systems and technology adoption behavior. The field of IT adoption research has been significantly shaped by his Unified Theory of Acceptance and Use of Technology (UTAUT series), a foundational model for analyzing how individuals adopt cutting-edge technologies. In the domain of higher education digitalization, these models have served as foundational frameworks for multiple studies, ranging from Jakkaew and Hemrungrote’s [16] early exploration to Abadie et al.’s [17] contemporary analysis.

Bandura was the David Starr Jordan Professor of Psychology at Stanford University. His research fields include psychology and education, focusing on the interaction between individual psychology and behavior and the environment. He is the proposer of the construction of social cognitive theory and self-efficacy theory. His conceptual models feature prominently in research addressing both learner psychology in digital education (e.g., [18]) and comparative studies of instructional models (e.g., [19]), particularly regarding college populations.

Creswell is a professor at the University of Michigan and has established himself as a leading authority in mixed methods methodology. His extensive publication record encompasses scholarly works addressing various research approaches, with particular emphasis on qualitative and mixed methods studies. Notably, he played a pivotal role in establishing the academic periodical dedicated to Mixed Methods Research. The author’s technical tools such as “analysis memo” and “triangulation” have become the standard practice in qualitative research and mixed methods research and are often used to gain an in-depth understanding of the exploration and understanding of instructional technology on the overall learning experience of learners, including college students’ sense of belonging, engagement, and self-confidence. Notable examples include Spencer et al. [20] and Evenhouse et al. [21], whose works demonstrate these applications. 3.1.2 Co-citation analysis of literature.

Two documents are said to be co-cited if they both appear in a third document. Co-citation analysis can be used to identify important documents in a field. In this study, there are five documents that have been cited more than 30 times (see Table 1). These documents together form the knowledge base for international higher education research in the digital age from three perspectives.

First, the research methods of international higher education in the digital age are explored. Braun and Clarke [22] explore the quality criteria of thematic analysis methods; analyze the diversity of thematic analysis in terms of paradigm, philosophy, and procedure; point out that common problems stem from the wrong assumption of homogenization of thematic analysis; and finally provide reviewers and editors with improvement guidelines in the form of 20 key questions to improve the quality of thematic analysis research on international higher education in the digital age.

Second, the technological development of international higher education in the digital age is reviewed. O’Flaherty and Phillips [23] used a scoping review method to review the research progress of flipped classrooms from 1994 to 2014, comprehensively combing relevant research and finding that flipped classrooms can cultivate lifelong learning skills for twenty-first century learners, However, future evaluations of the effectiveness of flipped classrooms should also include student engagement. Zawacki-Richter et al. [24] used a systematic literature review to analyze the application of AI in higher education from 2007 to 2018. The results show that AI is mainly used in academic support services and institutional and administrative services in higher education. However, there has been little critical reflection on AI in higher education, and the connection with theoretical teaching perspectives is weak. The ethical and pedagogical approaches to its application need to be further explored.

Finally, the teaching technologies commonly used in international higher education are clarified. Abeysekera and Dawson [25] provide a comprehensive definition of the flipped classroom model, expound its teaching principles, construct six testable propositions for theoretical argumentation, explore the impact of the flipped model on learning motivation and cognitive load, and call on future scholars to conduct in-depth research on the effectiveness of the flipped classroom. Hodges et al. [26] analyze the essential differences between emergency remote teaching (ERT) and online learning: ERT is an emergency measure that quickly switches traditional face-to-face teaching to a virtual format during a crisis, and it is usually not adequately prepared and designed; meanwhile online learning is a carefully planned and designed learning method that focuses on teaching interaction and learning outcomes and usually requires a longer preparation time.

From the results of the co-citation of authors and the co-citation of literature, there is no direct overlap between highly cited authors and highly cited literature, which shows that in the field of international higher education research in the context of the digital age, scholars’ research revolves around different core issues of the same topic, reflecting the diversity and richness of knowledge production in this field.

3.2.1 Digital teaching technologies

This theme includes four clusters, which mainly involve the use of different digital technologies in the field of international higher education in the context of the digital age, including artificial intelligence, virtual reality, digital technologies in flipped classroom, and so on. These digital teaching technologies are driving the digital transformation of higher education. Researchers are not only concerned about the impact of these digital teaching technologies on learning outcomes, but also concerned about learner engagement, satisfaction, and the personalized learning experience of learners.

The digital technologies used in flipped classroom in higher education institutions provide learners with multiple ways to access course content [27]. To explore the effectiveness of flipped classroom teaching strategies, Chiang et al. [28] developed a three-phase collaborative teaching model and conducted a case study of 29 graduate students. The results showed that collaborative teaching in flipped classroom can improve learner satisfaction, engagement, and collaboration. However, poor-quality pre-class videos and a lack of foreign language support can reduce student interest. Therefore, the researchers suggest that teachers should optimize learning materials and use interactive exercises to improve learning outcomes.

Intelligent teaching models formed by artificial intelligence and education provide new ways to improve teaching effectiveness and enrich the learning experience. An intelligent learning environment with personalization, intelligent interaction, and real-time feedback is an important factor in enhancing students’ active learning behaviors. In order to reveal the mechanism of students’ participation in intelligent teaching scenarios and promote their active learning, Wang et al. [29] found through empirical analysis that students’ willingness to participate, interactivity, and AI usefulness significantly affect their participation in intelligent teaching, and AI usefulness can also moderate the impact of students’ willingness to participate on their participation in intelligent teaching. Therefore, the researchers suggest adopting a learner-centered teaching method in intelligent teaching to promote students’ active participation and provide them with a better learning experience.

Wu and Wang [30] developed a VR creativity enhancement system based on haptic vibration feedback to explore how digital technology can improve students’ creativity. A comparison of the experimental group with the control group found that participants in the experimental group showed better results in terms of creative performance, attention level, and cognitive load. This study supports the 4P model of creativity and confirms that individual creativity can be enhanced through external technological interventions. Despite the increased cognitive load on the participants wearing VR equipment, they performed better in terms of creative performance and attention, which suggests that moderate cognitive challenge may promote creativity. In addition, the VR creative enhancement system developed in this study can be applied to teaching environments such as creative courses and art education to cultivate individuals’ innovative abilities and meet the complex challenges of the twenty-first century.

3.2.2 Digital teaching models

This theme includes three clusters, covering the three digital teaching models commonly used in international higher education, including distance learning, online learning, and blended learning. Researchers not only focus on learners’ adoption behavior, willingness to use, and influencing factors of digital technology but also analyze in depth the learning effectiveness and academic performance of learners under the digital teaching model, providing empirical support for the digital transformation of education.

Merhi [31] conducted an empirical investigation applying the technology acceptance model and innovation diffusion theory to examine determinants of podcast adoption among university students in distance education contexts. The research outcomes revealed that learners’ adoption intentions were significantly predicted by perceived utility and comparative benefits. Students believed that podcasts were a more effective review tool than textbooks because using podcasts reduced the need for them to take notes and they could access the course material frequently to review it. Also, as an effective tool to support learning, podcasts can provide a flexible and mobile learning experience, which increases the motivation and fun of learning.

Chen and Hwang [32] integrated the Unified Theory of Acceptance and Use of Technology to examine the effect of college students’ self-regulation on their behavioral intention to continue online learning. After assessing students’ self-regulation using a questionnaire, the researchers found that self-regulated metacognition and motivation could predict students’ future academic performance, effort expectancy, and social influence and that self-regulated metacognition could positively predict learners’ behavioral intention. This shows that self-regulation is an important part of online learning. In addition, students’ technology acceptance should be considered when assessing their willingness to continue online learning.

Escamilla-Fajardo et al. [33] point out that the increasing use of social media platforms in blended learning has promoted changes in the educational environment. However, there is currently little research on the effects of social media platforms on teaching and learning and student performance. In view of this, the researchers explored the implementation of TikTok in a university physical education course. The study found that TikTok, as a teaching tool, significantly enhanced students’ motivation, creativity, and curiosity and enhanced their learning experience by using music and movements that were aligned with the course content. Therefore, the researchers recommend introducing TikTok into physical science courses to enrich teaching methods. Despite the potential risks of TikTok, such as addiction, its educational value outweighs the risks.

3.2.3 Education quality and learner development

This theme includes four clusters related to the quality of higher education and learner development in the context of the digital age, including teacher education, self-regulated learning, learning outcomes, and post-secondary education. Related research explores how digital technologies can be used to support pre-service teacher training, self-regulated strategies of learners in AI-enabled educational environments, learning outcomes related to digital competencies, and the acceptance of online learning among teachers at the post-secondary level.

Eady et al. [34] investigated the effectiveness of an online synchronous platform for pre-service teacher training by administering a questionnaire to 58 pre-service teachers based on the EPEC conditions hierarchy (ease of use, psychologically safe environment, e-learning self-efficacy and competence). The results showed that platform ease of use was critical to the psychologically safe environment and e-learning efficacy of the training teachers, which in turn affected learning outcomes. In addition, course facilitator support and appropriate technological tools for training teachers were additional factors that influenced the online learning experience. Therefore, the researchers proposed the inclusion of instructor support and appropriate technological tools for training teachers in the original EPEC hierarchy to optimize online course design and improve learners’ online learning outcomes.

To explore how artificial intelligence can provide students with self-regulated learning skills in higher education, Koć-Januchta et al. [35] developed an AI e-textbook on biology for university students that integrates a knowledge base of 5000 concepts and algorithms and provides the possibility to ask questions and receive answers. The assessment of learning achievements revealed a significant positive association between students’ self-directed learning competencies and their ability to engage in profound, substantive learning when utilizing the AI textbook. Particularly in the biological sciences domain, learners demonstrated favorable responses to the AI system’s scaffolding in developing essential cognitive frameworks for knowledge acquisition.

Kohnke et al. [36] used a number of 1–6-minute microlearning activities to investigate the digital competence of higher education teachers, their use of digital technology, and the impact on their learning outcomes in the post-pandemic era. By watching micro-videos, the teachers generally reported that microlearning was flexible and stress-free, allowing them to focus on immediate tasks and improve their digital competence in a fragmentary way. In addition, some surveyed teachers also learned how to use digital resources in online and blended environments, equipping them with the necessary digital skills for teaching in the “new normal” of the post-pandemic era.

Salyers et al. [37] focused on the attitudes of adult learners returning to school toward online learning. Using a mixed research method, the researchers investigated the perceptions of students at post-secondary institutions (colleges and universities) in Canada toward online learning and assessed the impact of key components of online courses on the learner experience. The study found that most students taking online courses are female and that the navigational convenience of online learning platforms and course design significantly predict a positive online learning experience for learners. The results of this study are of great significance for the teaching of online learning in post-secondary education institutions around the world.

3.3.1 Distance education

“Distance education” is a keyword that has appeared frequently in 2014–2017. Its rise has been accompanied by changes on both the teacher and student sides, including changes in teachers’ curriculum design and teaching methods, as well as researchers’ concerns about student classroom participation and the quality of learning.

O’Shea et al. [38] pointed out that teachers’ distance teaching design should use appropriate technology to ensure that the content and presentation of online courses are attractive enough to improve students’ online learning engagement; Mkhize et al. [39] explored the acceptance and influencing factors of the e-learning system myUnisa among students at distance education universities in Africa. The study found that myUnisa’s compatibility with learning materials and the value it adds to the learning experience were the factors influencing students’ use of the LMS. Shelton et al. [40] focused on the student factors that contributed to failure in distance education courses in higher education institutions. The researchers found through empirical methods that the frequency of their classroom interactions, rather than the total amount of classroom interactions, was a better predictor of academic performance. Therefore, frequent and timely interactions between teachers and students are crucial to improving student academic performance and reducing dropout rates in distance education.

3.3.2 Teaching/learning strategies

“Teaching/Learning strategies” have been prominent in research from 2014 to 2018. Researchers have aimed to improve the learning experience of learners and explore the relationship between teaching/learning strategies and learning outcomes in a variety of digital teaching scenarios.

Jaggars and Xu [41] investigated teaching and learning strategies in online teaching platforms. The researchers found that teachers regularly reminding students and responding to questions in a timely manner, and students actively participating in interactions and providing class feedback on their own initiative, can lead to a better student online learning experience and academic performance. Viegas et al. [42] focused on teaching and learning strategies in remote laboratories based on virtual reality technology and found that teachers’ motivation of students to use virtual laboratories can overcome the barriers to use for beginners. Yilmaz and Keser [43] pointed out that the key to improving students’ learning experience is not to adopt new teaching methods but to improve and improve the teaching strategies used in the teaching and learning process through a reflective approach. Therefore, researchers focused on the learning strategies of students who reflectively used podcasts for distance learning and found that their reflective thinking activities such as self-questioning, planning their learning progress, and evaluating the process contributed to their academic success.

3.3.3 Flipped classroom

The “flipped classroom” became a buzzword between 2017 and 2020. Researchers focused on the flipped learning design model, the adaptability of the teaching subject to the flipped classroom, and the role of the flipped classroom in preventing academic cheating.

Lee et al. [44] developed a flipped learning design model for higher education and successfully implemented it in a university. The results showed that the flipped learning design model developed by the researchers could improve teaching quality and student satisfaction. Simmons et al. [45] also studied how to adapt teachers who are used to face-to-face teaching methods to flipped classroom teaching and proposed some practical measures, including providing professional development training for teaching staff, determining teaching topics suitable for flipped classroom, and regularly assessing student learning; Etgar et al. [46] studied academic cheating among students in higher education institutions and suggested using flipped classroom to prevent academic cheating. In a flipped classroom, the teacher helps students construct knowledge through discussion and guidance and customizes the content and methods of learning according to the abilities of students, thereby reducing students’ academic cheating.

3.3.4 Intention

“Intention” is a keyword that will appear frequently in 2018–2020. Researchers are mainly concerned with learners’ acceptance of the use of different digital technologies in teaching and their behavioral intentions, including online learning services, virtual learning tools, massive open online courses, etc.

Rajak et al. [47] studied the factors affecting college students’ intention to use online learning services. Through multiple regression analysis, it was found that this intention is affected by a combination of factors, including instructor characteristics, course content, and individual learner perceptions. Valencia-Arias et al. [48] proposed the eLearning Technology Acceptance Model (eLTAM) and studied college students’ acceptance of virtual learning tools and key factors. The results show that teacher preparation, learning autonomy, and self-efficacy are the main factors affecting students’ intention to adopt e-learning tools. Massive open online courses (MOOCs) are considered to be a platform for promoting educational equity, but their usage and completion rates among college students in developing countries are poor. Mohan et al. [49] used an extended unified theory of technology acceptance and use to explore the factors influencing Indian students’ intention to use MOOCs. The results show that usage habits and the content of the platform courses are the main predictors of Indian college students’ intention to use MOOCs.

3.3.5 Mobile learning

“Mobile learning” has a high incidence in 2020–2021. Research mainly involves the current situation and related impacts of flexible learning using mobile electronic devices, mobile programs, and mobile virtual reality devices by learners.

Sugden et al. [50] designed a series of online activities to study the participation and depth of learning of college students in mobile learning. Through a mixed research method, it was found that mobile learning promoted learners’ emotional, cognitive, and behavioral participation and stimulated their deep learning. Kumar et al. [51] conducted qualitative interviews with teachers and students using the Google Classroom mobile app and found that students regarded it as an online learning community with a peer group and usually used functions such as note viewing, classroom feedback, assignment submission, and so on, and teachers believed that the mobile app had simplified teacher-student communication and course management, bringing innovation to teaching; Sprenger and Schwaninger [52] studied the technological acceptance of mobile virtual reality devices by learners and found that despite its lower cost, mobile VR was not as popular as higher-quality, computer-based VR systems in educational settings. After using mobile virtual reality devices, learners’ perceived usefulness and behavioral intention decreased.

3.3.6 Identity

“Identity” is a buzzword in the period 2022–2024. Researchers focus on the transformation and shaping of the identities of academic researchers, teachers, and students brought about by digital technology in the field of higher education.

Butson and Spronken-Smith [53] focus on the impact of artificial intelligence on the academic identity of researchers, exploring the ethical, methodological, and epistemological issues of artificial intelligence in higher education and revealing the relationship between the efficiency and morality of academic researchers and innovation and integrity under the influence of artificial intelligence. Dai et al. [54] explore the transformation of teachers’ identities as they transition from face-to-face teaching to virtual educational worlds. Teachers believed that the new teaching environment provided more possibilities for student-teacher interaction and reshaped their identity, enhancing their sense of professional identity. Ou et al. [55] adopted a post-humanist perspective to examine the impact of students’ use of artificial intelligence language tools on their identity. The research demonstrated that regular utilization of AI-powered writing assistants (e.g., ChatGPT) in scholarly composition significantly enhanced students’ academic discourse competence and facilitated individual linguistic progress. These intelligent systems effectively converted traditional writing tasks into dynamic learning environments, enabling learners to adopt innovative roles as digitally enhanced knowledge constructors.

3.3.7 Support

“Support” is expected to be a keyword with a high frequency of occurrence between 2022 and 2024. Researchers are mainly concerned with the support and assistance provided by digital technologies such as artificial intelligence and virtual reality for student development, and most of the research results indicate that digital technologies have a positive impact on student development.

Rienties et al. [56] used a mixed research method to explore what support distance learners desire from AI digital assistants. The study found that distance learners hoped that AI digital assistants could provide instant academic support, emotional and social support, and so on; Yang et al. [57] studied how generative AI can support students’ active deep learning. The study found that students not only used generative AI to assist with coursework but also actively explored the conversational functions of generative AI to complete reading and writing tasks and develop reflective learning methods. Agbo et al. [58] developed a virtual reality-based game program and evaluated its support for students’ computational thinking. The experiment found that the VR game program supported students’ computational thinking and problem-solving skills better than traditional methods, and the immersion provided by VR also enabled students to obtain higher cognitive benefits from computational thinking.