Internet and Higher Education最新文献

Online courses emerged as an important mode for large-scale cross-national teachers' professional learning. However, with most previous research on teacher online professional learning (TOPL) focusing on resource-rich and technology-advanced regions, little attention has been paid to the factors influencing the online learning completion of college teachers in Global South contexts. This study aimed to explore the facilitators and inhibitors of this population's online learning completion in a cross-country program. In seven courses, individual, institutional, and country-level data of 3529 teacher-learners from 99 countries were collected. Forty-two learners were further interviewed. We adopted hierarchical linear modeling to analyze the nested relationships among the individual/institutional/country-level factors and course completion. Results revealed several significant associations between individual/institutional/country-level variables and course completion, as well as several moderation effects. Interviews complemented the analytics results. This study uncovers influential factors of TOPL in Global South contexts and provides practical implications for college teachers' online professional learning.

This study examined the impact of an artificial intelligence (AI)-supported approach to peer feedback provision on the feedback quality and writing ability of English as a foreign language (EFL) student reviewers. The researchers integrated an AI chatbot named Eva into an online peer review system to assist students in generating feedback. A total of 124 Chinese undergraduate students participated in nine peer review tasks over three weeks, with 64 students in the experimental group (using Eva) and 60 students in the control group (without AI support). Pre- and post-tests were conducted to assess the quality of student reviewers' peer feedback and these feedback providers' writing performance before and after the intervention. The findings revealed that the intervention significantly enhanced students' feedback quality. Additionally, the study showed that the proposed approach improved feedback providers' writing ability. This research underscores the potential of AI technology in enhancing EFL writing instruction.

The rise of microlearning both for professional training and in the field of education seems unstoppable. Nonetheless, there is a lack of evidence of its learning effectiveness and student satisfaction. The purpose of this paper is to uncover these two aspects of microlearning when taking part in a business education program. Its originality is that it analyses in depth a fast-growing EdTech startup that provides business training using microlearning methods, exploring the effect in terms of student satisfaction and learning effectiveness when combining a significant number of microlearning lessons to create a macro-learning course. Findings show that learning effectiveness is mainly explained by the reason for enrolling in this type of training and its applicability to the students' current jobs, resulting in four possible learning outcomes of increasing levels of effectiveness: entertainment, updating knowledge and skills, unexpected learning, and effective learning. This paper helps fill a gap in the research on learner satisfaction and microlearning effectiveness, finding that they are not necessarily guaranteed. It also has practical implications for designing, recruiting for, and implementing microlearning-based programs.

Keypoints: Empirical research into microlearning effectiveness and student satisfaction in postgraduate business education. Exploring the effectiveness of macro-learning, or the grouping of a significant number of microlearning lessons into a learning program. Uncovering different levels of learning effectiveness and their antecedent conditions.

The number of research doctorate degrees awarded by US institutions per year has increased steadily over the decades. However, the academic job market is also becoming more competitive, and doctoral candidates often face difficulties in developing a professional identity and making career-related decisions. In this study, we investigated PhD students' professional identity formation with regard to their usage of social networking sites (SNSs). Through semi-structured interviews with 16 students in Human-Computer Interaction (HCI), we found that self-presentation and online presence in the online community were considered necessary. Students' perception of using SNSs for professional activities was impacted by their peers and faculty. SNSs helped students gain information and support from online communities and also reflected their professional identities. The results present insights for transforming doctoral education and preparing students for diverse career options in today's economy.

ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed.

Smartphone distraction is pervasive in university classrooms, yet our understanding of its determinants remains incomplete. Drawing on complexity theory and the technology–personal–environment framework, this study employs multiple data sets for exploring the primary predictors and their configurations in determining in-class smartphone distraction among university students. Based on the interview data from 15 undergraduate students, seven primary predictors of smartphone distraction were identified. Subsequently, the study delved into the questionnaire data collected from 563 Chinese university students and revealed four combinations of the predictors for in-class smartphone distraction through fuzzy-set qualitative comparative analysis. This study contributes to the literature on in-class smartphone distraction by revealing its complex nature and offering practical strategies for educational practitioners to counteract the adverse effects of smartphone distraction in university classrooms.

One of the most significant issues with online education is that students disengage and eventually drop out of the course due to their inability to remain active in the online environment. Thus, disengagement from online courses has been seen as an important obstacle to the successful continuation of the online learning process. This study aimed to empirically explore the disengagement from online courses with a proposed model. A structural model was tested to explain the causal relationship among disengagement, cyberloafing, self-regulation skills, and satisfaction in online learning. The study group consisted of 843 undergraduates from a midsized institution in Turkey who were enrolled in an online course at the time of the study. Results showed that cyberloafing and satisfaction were significant predictors of disengagement, while self-regulation had an indirect effect on it. The study's findings indicated that online instructors and educational policymakers should focus more on fostering satisfaction and enhancing students' self-regulation abilities while keeping cyberloafing under control to prevent disengagement from online courses in the age of digital transformation.

Students with learning disabilities meet difficulties in cognitive abilities that are likely to affect their learning, especially online learning. Online learning usually lacks efficient face-to-face monitoring and leads to poor learning outcomes; in this case, students' self-regulated learning in an online environment matter. However, Self-Regulated Learning (SRL) status remains unclear for those with learning disabilities and how their working memory and processing speed affect self-regulated learning. A total of 147 undergraduate students were recruited from three public and four private universities in Taiwan to join this study, and they completed a self-reported questionnaire and several psychological measures. Our results revealed significant differences in SRL features between typically developing Chinese undergraduates and those with learning disabilities in an online environment. Compared to the students with learning disabilities, typically developing students outperformed in metacognitive skills, time management, environmental structuring, and persistence. Help-seeking was comparable between both groups. Working memory significantly contributed to SRL in all students, whereas processing speed only significantly influenced SRL in students with learning disabilities. The findings of this study have important implications for educators, researchers, and instructional designers aiming to optimize online learning experiences and support, especially from the perspective of SRL, for all students, particularly those with learning disabilities.

Collaborative knowledge construction has been used in higher education to support student groups' collaborative learning activities through students' exchange, negotiation, and reflection of perspectives through peer communications. To support this process, collaborative learning analytics tools have been designed to collect and analyze collaborative process and performance data with a goal to provide actionable feedback and improve learning quality. However, few tools have demonstrated the mechanism and details about how students develop their perspectives during the collaborative knowledge construction process in higher education. To fill this gap, this research proposed a tool named Collaborative Argument Map (CAM) that creatively visualized different types of perspectives students proposed from the individual, peer, and group levels. This tool was further implemented in a graduate-level course in online collaborative writing activities in China's higher education, with a goal to support students' knowledge construction with peers. The summative and process-oriented learning analytics approaches were conducted to reveal the effects of CAM on students' collaborative perceptions, processes, and final products. Results showed that most students made substantive use of the CAM tool and reported positive perceptions of the tool. Further examinations verified the tool's positive effects on improving the students' cognitive engagement levels and the quality of their final collaborative writing products. This research provided practical implications for future CLA tool design and instructional implications for using this type of tool in collaborative learning in higher education.