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Yun Tang; Zhengfan Li; Guoyi Wang; Xiangen Hu – Interactive Learning Environments, 2023
To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with…
Descriptors: Electronic Learning, Learning Activities, Self Management, Intelligent Tutoring Systems
Qian Fu; Wenjing Tang; Yafeng Zheng; Haotian Ma; Tianlong Zhong – Interactive Learning Environments, 2024
In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…
Descriptors: Predictor Variables, Psychological Patterns, Programming, Self Management
Zhonggen Yu; Wei Xu; Paisan Sukjairungwattana – Interactive Learning Environments, 2024
With the development of information technologies, many learners opt to stay home receiving various forms of online education such as massive open online courses (MOOC). However, many learners and instructors complain that MOOC-based learning effectiveness has been dampened by many factors. Through a meta-analysis using Stata/MP 14.0, this study…
Descriptors: MOOCs, Outcomes of Education, Influences, Instructional Effectiveness
Seongyune Choi; Yeonju Jang; Hyeoncheol Kim – Interactive Learning Environments, 2024
Intelligent Personal Assistants (IPAs) are becoming more prevalent in daily and educational contexts, increasing the possibility of using them as learning partners that can provide more personalized and learner-centric learning opportunities. However, research has primarily focused on educational advantages that IPAs may provide, overlooking…
Descriptors: Intelligent Tutoring Systems, Foreign Countries, Technology Uses in Education, Independent Study
Poitras, Eric G.; Doleck, Tenzin; Huang, Lingyun; Dias, Laurel; Lajoie, Susanne P. – Interactive Learning Environments, 2023
This study applies a time-driven approach to model self-regulated learning (SRL) on the basis of elapsed time metrics in the context of open-ended learning environments (OELEs), specifically, network-based tutors. In doing so, we examine how students allocated attentional resources to distinct phases of SRL as a measure of depth of information…
Descriptors: Independent Study, Self Management, Time, Networks
Li, Huiyong; Majumdar, Rwitajit; Chen, Mei-Rong Alice; Yang, Yuanyuan; Ogata, Hiroaki – Interactive Learning Environments, 2023
Self-directed learning (SDL) ability, its usefulness in higher education and life-long learning have been highlighted in previous literature. However, there has been much less understanding of the effects of SDL ability in the school settings, specifically the effects on learners' SDL behaviors and processes. To address this limitation, this study…
Descriptors: Junior High School Students, Independent Study, Student Behavior, Reading Achievement
Shuyun Han; Zhaoli Zhang; Hai Liu; Weiliang Kong; Zengcan Xue; Taihe Cao; Jiangbo Shu – Interactive Learning Environments, 2024
Online learning democratizes and provides flexibility in accessing education, but it also places greater demands on students' self-regulated learning strategies. Previous studies have explored the important impact of effort regulation on academic performance. However, there remains more to explore on the dynamics of effort regulation among…
Descriptors: Learner Engagement, Academic Achievement, Student Behavior, Self Management