Analyzing student performance in distance learning with genetic algorithms and decision trees

D Kalles, C Pierrakeas - Applied Artificial Intelligence, 2006 - Taylor & Francis
Applied Artificial Intelligence, 2006Taylor & Francis
Students that enroll in the undergraduate program on informatics at the Hellenic Open
University (HOU) demonstrate significant difficulty in advancing beyond the introductory
course. We have embarked in an effort to analyze their academic performance throughout
the academic year, as measured by homework assignments, and attempt to derive short
rules that explain and predict success or failure in the final exams. In this paper we review
previous approaches, compare them with genetic algorithm-based induction of decision …
Students that enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulty in advancing beyond the introductory course. We have embarked in an effort to analyze their academic performance throughout the academic year, as measured by homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm-based induction of decision trees, and argue why our approach has a potential for developing into an alert tool.
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