Date of Award
12-2015
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering and Sciences
First Advisor
Philip K Chan
Second Advisor
Marius Silaghi
Third Advisor
Joshua Pritchard
Fourth Advisor
J. Richard Newman
Abstract
In this study we investigate the correlation between student behavior and performance in online courses. Based on the web logs and syllabus of a course, we extract features that characterize student behavior. Using machine learning algorithms, we build models to predict performance at end of the period. Furthermore, we identify important behavior and behavior combinations in the models. The result of prediction in three tasks reach 87% accurate on average without using any score related features in the first half of the semester.
Recommended Citation
Mori, Makoto, "Identifying Student Behavior for Improving Online Course Performance with Machine Learning" (2015). Theses and Dissertations. 682.
https://repository.fit.edu/etd/682