Identify and Help At-Risk Students Before It Is Late
Soohyun Nam Liao · 2016
Identifying at-risk students early in the term is valuable. It is because an instructor can have more time to provide extra support, and students can also estimate how much extra effort they should put on to succeed in class. Prior work showed it is possible to predict at-risk students, but they either did not provide a specific prediction method or are too onerous to implement. Thus, my dissertation will develop and evaluate more robust, universal, and simple prediction methodology to classify at-risk students and propose how to automatically generate customized practice materials for early intervention. Once the methodology becomes robust, I will implement publicly accessible educational software application so that other CS instructors can easily adopt this method.