Risk Analysis Commons^™

Understanding The Privacy Methods, Implications, And Challenges Of Educational Data Mining., Tyler Blanco, Liam Smith

CURCE Annual Undergraduate Conference

Studies on educational data mining have massively advanced the analytical understanding of student learning, outcome predictions, student modeling, and better evaluation. While the information retrieved can improve educational outcomes, such studies must not break the wall of trust or compromise student privacy. To this end, it’s imperative to understand the methods of how privacy is ensured in the field of educational data mining and learning analytics, the limitations of these strategies, the complications that may follow, and the possible resolutions. In this project, we take a systematic approach to review studies in the educational data mining field. Initially, we examine …