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.