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This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether media richness and social interaction as environmental feature antecedents to nurses’ learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs) and task performance.

Sample data for this study were collected from nurses at five university-/medical university-affiliated hospitals in Taiwan. A total of 500 questionnaires were distributed, and 303 (60.6%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that nurses’ perceived media richness and social interaction in MOOCs positively influenced their behavioral LE and psychological LE elicited by MOOCs, which jointly caused their continuance intention of MOOCs and, in turn, enhance their task performance. The results support all proposed hypotheses and the research model, respectively, explains 84.3% and 63.7% of the variance in nurses’ continuance intention of MOOCs and task performance.

This study uses the S-O-R model as a theoretical base to frame nurses’ continuance intention of MOOCs and task performance as a series of the internal process, which is affected by environmental stimuli (i.e. media richness and social interaction) and organismic states. Noteworthily, while the S-O-R model has been extensively used in prior literature, little research uses this paradigm to expound nurses’ continuance intention of MOOCs in the work settings. Besides, there is a dearth of evidence on the antecedents of nurses’ task performance in the context of MOOCs. Hence, this study’s empirical evidence contributes significantly to the existing literature on bridging the gap of limited evaluation for the research on the impact of nurses’ MOOCs learning on their task performance in the work settings, which is very scarce in the S-O-R view.

In the era of Industry 4.0, the relevance of webinar tutorials, a form of distance learning, is paramount. These tutorials can catalyze self-regulated learning, critical thinking and communication skills, especially for prospective and in-service teachers pursuing higher education. This paper aims to develop a conceptual framework and report the results of implementing a flipped classroom with whiteboard animation and modules. This innovative approach seeks to enhance students' self-regulation, critical thinking and communication abilities.

This study employs a mixed-methods approach. In the first phase, a hypothetical model and conceptual framework for the Flipped Classroom with Whiteboard Animation and Modules were developed to enhance self-regulation, critical thinking and communication skills. The resulting conceptual framework was then implemented through a quasi-experiment using a non-equivalent control group design involving 83 elementary school teachers enrolled in the Elementary School Science Education course (PDGK4202), divided into three treatment groups. Qualitative data were collected through observations of the learning process, documentation of student worksheet completion and interviews with students. Questionnaires and tests were used as instruments for quantitative data collection. Qualitative data were analyzed using descriptive methods, while quantitative data were evaluated using MANCOVA.

The findings demonstrate significant improvements in students' self-regulation, critical thinking skills and communication abilities after implementing the Flipped Classroom with Whiteboard Animation and Modules.

Some limitations in this study need to be recognized. These limitations include the specific sample type of elementary school teachers who went back to college to take science learning courses in elementary school. Teachers have various diversity that may affect the dependent variable, such as age, educational background, facilities, internet signal stability at their learning location and teaching experience. This study was conducted in a specific context (using the flipped classroom model at UT), so the results may need to be more generalizable to other educational contexts with different facilities, systems and policies. In addition, the measurement of self-regulation and communication skills, particularly with questionnaires, relies on self-report, which can be biased due to socially desirable responses or inaccurate self-assessment. Although the MANCOVA test showed significant results, it is possible that other variables not controlled for in this study (e.g. intrinsic motivation, social support from family or colleagues) also affected the independent variables.

This study emphasizes the importance of adapting webinar tutorials for Industry 4.0 and enhancing self-regulated learning, critical thinking and communication skills, particularly for working students and teachers. It offers a practical framework for educators and suggests ways to improve online learning materials. The implementation results show significant skill enhancement. These findings have practical implications for educators, institutions and instructional designers, guiding the development of effective distance learning strategies and curriculum improvements in the digital age.

The social implications of this study are noteworthy. In the context of Industry 4.0, adapting webinar tutorials to promote self-regulated learning, critical thinking and communication skills is essential not only for the educational sector but also for the broader society. It equips prospective and in-service teachers, who are pivotal in shaping future generations, with the necessary skills to navigate a rapidly changing digital landscape. Furthermore, enhancing self-regulation and critical thinking abilities among employed students contributes to a more informed and adaptable workforce, fostering societal resilience in the face of technological advancements.

The uniqueness of this study stems from the creative modification of a webinar tutorial, which specifically targets the urgent requirement for enhancing abilities among teachers and university students. The conceptual framework serves as a valuable tool for educators, and the findings of this study confirm its effectiveness in enhancing self-regulation, critical thinking abilities and communication proficiency. Furthermore, the recommendations offered also furnish practical insights to improve this model.

The study explored how PSTs perceived a learning design, using a decision simulator, a self-reflection guide and a peer mentoring guide as tools to mediate reflections on approaches to the teacher’s role. The individual characteristics of PSTs were also considered, including role clarity, self-efficacy and affective commitment, as predictors of the learning design’s perceived relevance.

Informed by earlier research exploring the teacher’s role, a scenario-based simulation was supported by a tool for self-reflection on the results and a peer mentoring tool for the further development of an individual’s teaching role. Using structural equation modelling, the study assessed the statistical strength of the relationships between relevant factors to explore how a decision simulator and associated research-based tools were perceived by preservice teachers (PSTs).

The results indicate that regardless of PSTs’ individual characteristics, the decision simulator and associated peer mentoring tools have the potential to enhance learning and reflection. Therefore, the proposed approach can facilitate peer mentoring and enhance PSTs’ learning potential.

The paper explores the use of innovative approaches to mentoring by introducing peer mentoring grounded in experiences using a decision simulator.

In the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.

A survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.

Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.

This study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.

The practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.

This study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.

The purpose of this conceptual paper is to define and explore the roles of mentors, the responsibilities and even misconceptions of their position in partnership schools, the characteristics of effective mentors, the gaps that exist in current mentor training and the need for updated and forward-thinking flexible and accessible mentor training as it relates to improvements in University Teacher Preparation Programs and their partnership schools.

The authors detail the process of interviewing a diverse population of mentors and teacher candidates (TCs) who have participated in our professional development (partnership) schools. See Appendices A and B for parallel questions posed during each interview process. Following the interviews, data were gathered in both text and video-based formats to create mentor training video modules to improve the current state of mentor training in our program. Themes were identified following an analysis of both interview intakes, and modules were created to align with these themes.

Following the first rounds of implementation, the authors have reflected and noted that a need for an even more diverse population of both mentors and interns to be interviewed is necessary moving forward. The authors do note an appreciation by mentors and university partners in the quality, flexibility and accessibility that this new video module-based mentor training program provides.

The video-based mentor training modules that the authors detail honor the needs of both new and returning mentors. Via the interviews with teacher candidates, mentors are able to empathize and understand how to be better mentors to their future TCs. Via the scenarios and questions that follow each thematically driven module, mentors are able to independently reflect on their current practices and ways to improve their roles. At any point in the internship year, mentors can return to the video training modules to review and thus improve their practice.

Mentors, university supervisors, teacher candidates and site coordinators (those individuals who identify mentors in their buildings) note improvement in their ability to communicate effectively as a result of being presented with video modules and reflections about the role of mentors from both mentors and TCs.

The authors were called on by leadership in the College of Education at the University to create this original mentor training video module. It is unique to the College of Education at the University. Great value exists in its accessibility, adaptability (we can upload new videos at any time) and representation of both the mentor and TC perceptions, suggestions and experiences in our program.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

The purpose of this systematic literature review is to catalog, explore and disseminate knowledge developed related to the affordances, constraints and collaborative practices of e-mentoring in order to offer recommendations for mentoring programs.

After identifying studies utilizing inclusion and exclusion criteria and a variety of relevant databases, chosen studies were organized into three categories based on e-mentoring practices.

Results reflect chat-room-based e-mentoring and e-mentoring with a video component both to have various affordances and collaborative features. These forms of e-mentoring can potentially be relevant to new teachers’ needs, may promote community and are disconnected from mentees’ school systems. A third form of e-mentoring, virtual reality e-mentoring, however, has the potential for accessibility issues and lack of relevancy to the mentoring process.

Chat-room-based e-mentoring and e-mentoring with a video component may benefit new teachers who do not have access to in-person mentors. However, these forms of e-mentoring may necessitate a component that ensures mentors and mentees alike remain consistently engaged in the e-mentoring process.

This study aims to explore students’ perceptions of the use of an artificial intelligence-generated content avatar (AIGC avatar) within a learning management system (LMS).

This qualitative research involved seven postgraduate students. Data were collected through individual, in-depth interviews. The videos of the AIGC avatar, created using Leonardo, ChatGPT and Heygen, were uploaded to the LMS to communicate with students for the purposes of a welcome note, assignment guide, assignment feedback, tutorial reminders and preparation as well as to provide encouragement and study tips. Students were interviewed at the end of the semester.

The findings of this study indicated that the majority of participating students held positive perceptions regarding the use of the AIGC avatar in the LMS. They reported that it enhanced their perceived instructor’s social presence and motivation to learn. The assignment guide and feedback were particularly valued by the participants. While some students noted the AIGC avatar’s lack of naturalness, others appreciated the clear and professional speech it delivered.

The study was confined to seven students from a single course at one institution, which may limit the generalizability of the findings. Future research could involve a larger and more diverse group of participants.

The findings may offer education providers an alternative solution for engaging students in an LMS.

This study highlights the potential of AIGC avatars to replace text-based communication in LMS and enhance students’ perceived instructor social presence.

This systematic literature review paper critically examines the effectiveness of screencast feedback compared with text feedback in promoting student learning outcomes in online higher education. This paper aims to contribute to the ongoing discussion surrounding feedback modalities and their impact on online learning environments.

This paper adopts a systematic review approach to synthesise and analyse existing studies investigating the use of screencast feedback in online higher education settings. A comprehensive search and selection process was employed to identify relevant literature. The selected studies were then analysed for their methodologies, findings and implications. This paper seeks to provide an overview of the current state of research, highlighting the benefits, challenges and potential impacts of screencast feedback on student learning outcomes.

The findings of this paper suggest that while there is a positive perception of screencast feedback among students and instructors, drawing definitive conclusions about its superiority over text feedback remains at the very beginning. Students generally appreciate the personalised, supportive and engaging nature of screencast feedback, particularly within the online learning context. However, challenges such as technical barriers and potential workload implications for instructors are also noted. Further empirical research is needed to comprehensively evaluate the comparative efficacy of screencast feedback, considering factors like online engagement, digital literacy and the impact on diverse student populations.

This review underscores the acute necessity for expansive and meticulously designed studies that can provide conclusive insights into the authentic potential of screencast feedback and its resonance within the unique landscape of online learning. Through rigorous inquiry, educators can discern the optimal strategies for harnessing the advantages of screencast feedback to enhance student learning outcomes, aligning harmoniously with the dynamics of virtual classrooms.

Screencast feedback emerges as a promising avenue to foster meaningful connections between instructors and learners. The review highlights that screencast feedback engenders a more dialogic interaction between lecturers and students, resulting in personalised, supportive and engaging feedback experiences.

The systematic review conducted underscores the positive reception of screencast feedback from both students and lecturers in this context. The findings are consistent with the principles of social constructivist theory, suggesting that the interactive and personalised nature of screencast feedback facilitates a richer educational experience for students, even within the confines of virtual classrooms (Vygotsky, 1978).

This innovative blend of methodologies contributes new insights that can inform educational practices and pedagogical strategies in online learning environments.