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Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
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Li, Shuang; Wang, Shuang; Du, Junlei; Pei, Yu; Shen, Xinyi – Journal of Computer Assisted Learning, 2022
Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course…
Descriptors: Online Courses, Time Management, Time Factors (Learning), Learning Strategies
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Jingjing Zhang; Yicheng Huang; Bo Yu – Interactive Learning Environments, 2024
The global expansion of online language courses is on the rise, offering greater flexibility in the approaches to learning design. This study delves into the exploration of the fundamental learning design patterns within a K-12 synchronous language course. Leveraging a rich array of textual data, including syllabi, textbooks, course outlines,…
Descriptors: Learning Analytics, Instructional Design, Elementary Secondary Education, Second Language Instruction