Abstract:

Developing tools to support students and learning in a traditional or online setting is a significant task in today’s educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student’s performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poorperforming students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered several human interpretable features that quantify these factors. These features were derived from the students’ grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance

Description:

Svm algorithm, Decision tree algorithm, Random Forest algorithm, Gradient Boosting algorithm