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Lemay, David John; Doleck, Tenzin – Interactive Learning Environments, 2022
Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…
Descriptors: MOOCs, Academic Achievement, Prediction, Student Behavior
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Zheng, Juan; Xing, Wanli; Huang, Xudong; Li, Shan; Chen, Guanhua; Xie, Charles – Interactive Learning Environments, 2023
Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the…
Descriptors: Engineering Education, Achievement Gains, Knowledge Level, Learning Strategies
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Hong, Jon-Chao; Hwang, Ming-Yueh; Liu, Yeu-Ting; Lin, Pei-Hsin; Chen, Yi-Ling – Interactive Learning Environments, 2016
Educational games can be viewed in two ways, "learning to play" or "playing to learn." The Chinese Idiom String Up Game was specifically designed to examine the effect of "learning to play" on the interrelatedness of players' gameplay interest, competitive anxiety, and perceived utility of pre-game learning (PUPGL).…
Descriptors: Educational Games, Prediction, Anxiety, Competition