Abstract
The barriers to accessing electronic learning materials have significantly decreased in the rapidly evolving field of education technology. Learning Analytics (LA) has emerged as a valuable tool for improving institutional decision-making and student outcomes. This paper addresses the lack of detailed research focusing on the foundational stages of LA in Canadian universities by developing an automatic LA system to identify engineering students needing support. The initial phase involves examining LA practices in other institutions and evaluating the current landscape of LA implementation at scale among Canadian universities. Additionally, the paper explores the limitations of the LA features in Brightspace Learning Management System (LMS), specifically the Student Success System (S3), for detecting at-risk students and providing interventions. While statistical results demonstrating the effectiveness of LA in Canadian universities are lacking, proactive strides have been made, and more than 50% of universities investigated have implemented or planned to implement LA tools. This underscores the growing recognition of the need for LA to enhance academic performance and student success. The paper concludes by proposing a robust LA architecture as a reference model that prioritizes early identification of at-risk students which tackle the limitations of current LA features in Brightspace LMS.
Published Version
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