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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
Yun-Qi Bai; Ya-Qian Xu; Jian-Jun Xiao – Interactive Learning Environments, 2024
This study takes the value-based adoption model and CIE model of the learning process as the theoretical basis and combines them to explore the influencing factors and mechanisms of learners' online interaction and perceived value. Based on the questionnaire survey data of 81 learners' potential factors and their 45,166 real-time behavior data on…
Descriptors: MOOCs, Interaction, Student Behavior, Learning Processes
Mubarak, Ahmed Ali; Ahmed, Salah A. M.; Cao, Han – Interactive Learning Environments, 2023
In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…
Descriptors: MOOCs, Learner Engagement, Performance, Student Behavior
Bai, Yun-Qi; Xiao, Jian-Jun – Interactive Learning Environments, 2023
As a representative practice of the theory of connectivism, cMOOCs emphasize learners' content-based connective learning. Effectively promoting learners' content production is the focus of cMOOC research and practice. This study explores whether and how learners' online interactions affect the content production of courses. Based on 45166…
Descriptors: MOOCs, Students, Foreign Countries, Learner Engagement
Cixiao Wang; Yaqian Xu – Interactive Learning Environments, 2024
Different from the group formation approaches led by teachers, learners' generative learning objectives and the independent choice of collaborative partners are important in the Internet learning environment. This study takes the cMOOC (connectivist massive open online course) 5.0 "Internet + education: dialogue between theory and…
Descriptors: Open Education, Computer Mediated Communication, Cooperative Learning, Social Networks
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
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