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Chih-Yueh Chou; Wei-Han Chen – Educational Technology & Society, 2025
Studies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, including clustering and classification, to analyze…
Descriptors: Student Behavior, Help Seeking, Problem Solving, Information Retrieval
Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software
Conrad Borchers; Jiayi Zhang; Hendrik Fleischer; Sascha Schanze; Vincent Aleven; Ryan S. Baker – Journal of Educational Data Mining, 2025
Think-aloud protocols are a standard method to study self-regulated learning (SRL) during learning by problem-solving. Advances in automated transcription and large language models (LLMs) have automated the transcription and labeling of SRL in these protocols, reducing manual effort. However, while effective in many emerging applications, previous…
Descriptors: Artificial Intelligence, Protocol Analysis, Learning Strategies, Classification
Sakyiwaa Boateng; Sizwe J. C. Masuku – Journal of Baltic Science Education, 2025
Electricity and magnetism are fundamental areas of physics and are integral to science curricula at various educational levels. However, this area has been reported to contain several concepts that students find challenging, leading to perspectives that diverge from scientifically accepted views. This study examines the errors made by physics…
Descriptors: Physics, Science Teachers, Preservice Teachers, Teacher Education Programs

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