Towards Personalization of Student Learning and Engagement in a First-Year Undergraduate Course

Résumé fourni par la source

Advancements in classroom technology and data collection have allowed for new studies into how students interact with course material. This paper presents the development of a new tool designed to process timestamp information from a learning management system in a remote, synchronous course to analyze patterns of behaviour and predict student outcomes in the course. The timestamps are arranged to create a personalized timeline of activity for individual students, focusing on the length of time between successive interactions. Preliminary analysis of the timestamp intervals across a class of students over an entire term is also presented. The lengths of time between successive course interactions follows a long-tail distribution with peaks occurring at approximately 24-hour periods, implying that students were most likely to access course material at daily or multi-day intervals.

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