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Wang, Tingting; Zheng, Juan; Tan, Chengyi; Lajoie, Susanne P. – Journal of Computer Assisted Learning, 2023
Background: Computer-based scaffolding has been intensively used to facilitate students' self-regulated learning (SRL). However, most previous studies investigated how computer-based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of…
Descriptors: Metacognition, Scaffolding (Teaching Technique), Computer Assisted Instruction, Intelligent Tutoring Systems
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Yu, L. C.; Lee, C. W.; Pan, H. I.; Chou, C. Y.; Chao, P. Y.; Chen, Z. H.; Tseng, S. F.; Chan, C. L.; Lai, K. R. – Journal of Computer Assisted Learning, 2018
This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…
Descriptors: Prediction, Academic Failure, Models, Identification
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Alonso-Fernández, Cristina; Martínez-Ortiz, Iván; Caballero, Rafael; Freire, Manuel; Fernández-Manjón, Baltasar – Journal of Computer Assisted Learning, 2020
Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires-postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict…
Descriptors: Case Studies, Teaching Methods, Game Based Learning, Student Motivation