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Early Identification Of Students At Academic Risk Based On Learning Management System Log Data, Roger Sheng So
Early Identification Of Students At Academic Risk Based On Learning Management System Log Data, Roger Sheng So
Theses and Dissertations
Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify students who are at academic risk. The scope of this study is to retrospectively analyze first-year student activity for the Spring 2022 semester for early warning signs worthy of intervention. A student risk assessment will be determined by reviewing student LMS …