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Direct linkOsipenko, Maria – Education and Information Technologies, 2022
A data-driven model where individual learning behavior is a linear combination of certain stylized learning patterns scaled by learners' affinities is proposed. The absorption of stylized behavior through the affinities constitutes "building blocks" in the model. Non-negative matrix factorization is employed to extract common learning…
Descriptors: Behavior Patterns, Models, Undergraduate Students, Preferences
Xiaona Xia; Wanxue Qi – Education and Information Technologies, 2024
The full implementation of MOOCs in online education offers new opportunities for integrating multidisciplinary and comprehensive STEM education. It facilitates the alignment between online learning content and learning behaviors. However, it also presents new challenges, such as a high rate of STEM dropouts. Many learners struggle to establish…
Descriptors: Graphs, MOOCs, STEM Education, Learning Processes
Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
