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Biography

Prof.  Zhi  Liu
Central China Normal University,  China

Title: Automatic Profiling students' learning engagement in MOOC discussions to identify learning achievement

Abstract:

Learning engagement encompasses three fundamental dimensions—cognitive, emotional, and behavioral engagement—that intricately interact to jointly influence students' learning achievements. However, the interplay between multiple engagement dimensions and their correlations with learning achievement remain understudied, particularly across different academic disciplines. This study adopts an automated configurational approach that integrates bidirectional encoder representation from transformers (BERT) and fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of learning engagement, their connections with learning achievement, and variations across disciplines. Our analysis reveals a nuanced profile of learners’ learning engagement, indicating the high-achieving individuals demonstrated more frequent posting and commenting behaviors and the high-level cognitive engagement than low-achieving individuals. Second, our analysis revealed multiple configurations where the coexistence or absence of factors at different levels of the cognitive, behavioral, and emotional dimensions significantly impacted learning achievement. Learners who conducted posting and replying behaviors, expressed positive emotions, and engaged in deep cognitive engagement tended to achieve superior learning outcomes. Third, there were significant differences in behavioral and emotional engagement among learners across different academic disciplines. Specifically, pure discipline learners were more inclined to engage in posting behaviors than the applied discipline learners. Across academic disciplines, positive emotions correlated strongly with higher achievement. These findings deepen our understanding of the multifaceted characteristics of learning engagement in MOOCs and highlight the importance of disciplinary distinctions, providing a foundation for educators and designers to optimize learners' MOOC effects and tailor learning experiences in diverse disciplinary contexts.

Biography:

Zhi Liu is a senior fellow researcher/professor and PhD supervisor at the National Engineering Research Center of Education Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University. He also holds a position as a guest researcher at the Computer Science Institute, Humboldt University of Berlin. With deep expertise in text mining, sentiment analysis, educational data mining and intelligent tutoring systems, Liu has published over 60 SCI/SSCI indexed papers in top journals, including Knowledge-Based Systems, Computers & Education, Internet and Higher Education, and IEEE Transactions on Learning Technologies. In addition, he serves as a key member of the national expert database for graduate education evaluation, a peer review expert for the National Natural Science Foundation of China, and the principal investigator of National Natural Science Foundation and the National Key R&D Program of China (2030 Major Projects). Liu is actively involved in international academic communities, serving in various leadership roles including as the chair of the organizing committee for the ICET. He is a guest associate editor for the international journal Frontiers in Artificial Intelligence and sits on the editorial boards of Discover Education and Frontiers in Psychology, and holds the Lifetime Member status of the Chinese Association of Automation. His contributions have been widely recognized, earning him numerous awards including the First Prize of the Science and Technology Progress Award of Hubei Province in 2024, First Prize of the Teaching Achievement Award of Higher Education Institutions in Hubei Province in 2022, and the honor of being a Top 1% Highly Cited Scholar in China National Knowledge Infrastructure (CNKI) for 2024. 

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