  With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a
huge amount of data has been collected related to students' learning. With the
careful analysis of this data, educators can gain useful insights into the
performance of their students and their behavior in learning a particular
topic. This paper proposes a new model for predicting student performance based
on an analysis of how students interact with an interactive online eTextbook.
By being able to predict students' performance early in the course, educators
can easily identify students at risk and provide a suitable intervention. We
considered two main issues the prediction of good/bad performance and the
prediction of the final exam grade. To build the proposed model, we evaluated
the most popular classification and regression algorithms on data from a data
structures and algorithms course (CS2) offered in a large public research
university. Random Forest Regression and Multiple Linear Regression have been
applied in Regression. While Logistic Regression, decision tree, Random Forest
Classifier, K Nearest Neighbors, and Support Vector Machine have been applied
in classification.
