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MOOCs (massive open online courses) have attracted considerable attention from researchers. Fueled by constant change and developments in educational technology, the trends of MOOCs have varied greatly over the years. To detect and visualize the developments and changes in MOOC research, 4,652 articles published between 2009 and 2021 were retrieved from Web of Science and Scopus with the aid of CiteSpace. This study sought to explore the number of publications, co-citation network, cluster analysis, timeline analysis, burstness analysis, and dual-map overlays based on co-citation relationships. The first finding was that the number of publications on MOOCs had increased consistently, and grew especially quickly between 2013 and 2015. Second, the main topic of the top 10 co-cited studies revolved around the problem of learner continuance. Third, blended programs, task-technology fit, and comparative analysis have emerged as popular subjects. Fourth, the development of MOOC research has followed distinct phases, with 2009 to 2012 the starting phase, 2013 to 2015 the high growth phase, 2016 to 2018 the plateau phase, and 2019 to 2021 another peak phase. Lastly, both cluster analysis and dual-map overlays provided empirical evidence of cross-disciplinary research. Our findings provided an in-depth and dynamic understanding of the development and evolution of MOOC research and also proposed novel ideas for future studies.

Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face) collaborative learning were also compared. An analysis using structural equation modeling revealed that group member familiarity increased teamwork satisfaction, which in turn increased student engagement and perceived knowledge construction. A multi-group analysis revealed that while FtF collaborative learning exhibits higher levels of group member familiarity, teamwork satisfaction, student engagement, and perceived knowledge construction, the mediating effect of teamwork satisfaction was more pronounced in online settings. The study findings provided insights for teachers to improve collaborative learning experiences and adapt different teaching strategies.

Chatbots have shown great potential for language learning. However, previous studies have reported mixed results on the efficiency of chatbot-assisted language learning (CALL). This study integrated the results of previous experimental studies on CALL by using meta-analysis to explore its effectiveness. A total of 61 samples from 18 studies were examined. The results showed that CALL had a moderate average effect (g = 0.527). In addition, nine potential moderating variables (educational level, target language, language domain, learning outcome, instruction duration, chatbot interface, chatbot development, task dominance, and interaction way) were identified and discussed. The results of this study provided insights into the use and design of chatbots for language learning.

The insulated gate bipolar transistor (IGBT) is crucial in high-voltage applications due to its characteristics, like breakdown voltage (BV) and on-state voltage V CE(sat). However, its slower turn-off time, attributed to hole mobility, restricts its frequency range. Techniques such as the carrier storage layer (CSL) and super-junction (SJ) structures aim to optimize BV and V CE(sat) through hole density and field distribution. Combining CSL and SJ offers advantages, yet challenges remain regarding E-field concentration. In this work, the split CSL concept introduces a solution by optimizing BV and E off through effective field distribution and hole extraction acceleration respectively while maintaining V CE(sat). Split CSL, which is divided into a high doping layer (HDL) and a low doping layer (LDL), reduces the burden on the gate oxide by distributing the E-field evenly when in the off-state due to the difference in doping concentration. During the turn-off, the hole current is concentrated on the LDL, which has relatively low resistance, thereby accelerating hole extraction. Simulation-based results showcase improvements in the proposed structure's properties. Further optimization of HDL and LDL concentrations enhances the structure's performance. It is clear that the split CSL structure presents the potential for advancing IGBT capabilities. The application of the split CSL structure resulted in significant improvements: the turn-off time was reduced by 32.4% and the BV increased by 32.5 V compared to conventional CSL-SJ structures. These enhancements highlight the effectiveness of the split CSL design in optimizing the IGBT's performance attribute. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including "Large language model," "LLM," and "ChatGPT." Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services. [ABSTRACT FROM AUTHOR]

Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency. [ABSTRACT FROM AUTHOR]

Prior research has explored the impact of diverse anthropomorphic interventions on the effectiveness of AI (artificial intelligence) instructors. However, the exploration of interpersonal communication skills (e.g., self-disclosure) as anthropomorphic conversational cues for AI instructors is rare. Considering the positive impact of the self-disclosure of human instructors and guided by the social penetration theory (Altman & Taylor, 1973) and computers are social actors (CASA) paradigm (Nass & Moon, 2000), this study explores the role of self-disclosure by AI instructors and the mediating role of emotional attachment between AI instructors' self-disclosure and students' learning experiences (learning interest and knowledge gain). Additionally, it examines the differences in students' emotional attachment, learning interest, and knowledge gain between AI and human instructors. Through a 2 (AI instructor vs. human instructor) × 2 (self-disclosure: yes or no) experiment, this study concluded that 1) consistent with human instructors, self-disclosure by AI instructors led to higher emotional attachment, learning interest, and knowledge gain; 2) emotional attachment played an important mediating role in AI instructor self-disclosure and students' learning interest and knowledge gain; and 3) in the context of self-disclosure, students exhibited similar levels of emotional attachment to both AI and human instructors, with no significant differences observed. Regarding learning outcomes, while students demonstrated a greater interest in learning during courses taught by AI instructors, the difference in knowledge gained from AI and human instructors was not significant. The results of this study contribute to the understanding of the anthropomorphic cues of AI instructors and provide recommendations and insights for the future use of AI instructors in educational settings. [ABSTRACT FROM AUTHOR]