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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Liu, Yan; Deng, Lisa; Lin, Lin; Gu, Xiaoqing – Interactive Learning Environments, 2023
With the rapid development of mobile devices and web-based technologies, it becomes common for students to switch between different tasks during study time. However, it remains unclear how the transition between on-task and off-task states happens and how digital devices affect the process. This study examines college students' independent study…
Descriptors: Independent Study, Student Behavior, Behavior Patterns, Time on Task
Sarika Sharma; Jatinderkumar R. Saini – Interactive Learning Environments, 2024
During the COVID-19 pandemic period of almost two years, online teaching was adopted by Higher Educational Institutes (HEIs) mostly as an emergency measure to maintain endurance in teaching-learning activities in academics. Although a lot of research works have focussed on the teaching-learning strategies deployed during the pandemic period, the…
Descriptors: Online Courses, Electronic Learning, Cognitive Ability, Cognitive Style
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

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