Authors:
Muzaffer Ege Alper
and
Zehra Çataltepe
Affiliation:
Istanbul Technical University, Turkey
Keyword(s):
Educational Data Mining, Bayesian Logistic Regression, Feature Selection, Course Grades, ABET Outcomes, Computer Engineering Education, mRMR (Minimum Redundancy Maximum Relevance), Feature Selection.
Related
Ontology
Subjects/Areas/Topics:
Assessment and Accreditation of Courses and Institutions
;
Computer-Aided Assessment
;
Computer-Supported Education
;
Course Design and e-Learning Curriculae
;
e-Learning
;
e-Learning in Electrical, Mechanical, Civil and Information Engineering
;
Learning/Teaching Methodologies and Assessment
;
Social Context and Learning Environments
Abstract:
Modeling and prediction of student success is a critical task in education. In this paper, we employ machine learning methods to predict course grade performance of Computer Engineering students. As features, in addition to the conventional course grades we use fine grained student performance measurements corresponding to different goals (ABET outcomes) of a course. We observe that, compared to using only previous course grades, addition of outcome grades can significantly improve the prediction results. Using the trained model enables interpretation of how different courses affect performance on a specific course in the future. We think that even more detailed and systematically produced course outcome measurements can be beneficial in modeling students university performance.