Abstract. This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection.
Within the year 2003, B. Minaei-Bidgoli, William F. Punch REF presented an approach for classifying the students in order to predict their final grade based on features extracted from log data of an education web based system.
Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System
IC-AI
Computer Science