Internet and Higher Education最新文献

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.

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.

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.

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.

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.