Enhancing Online Learning Performance: An Application of Data Mining Methods

B. Minaei-Bidgoli, G. Kortemeyer, and W.F. Punch (USA)


Web-based Educational System, Data Mining tasks,Classification fusion, Genetic Algorithm


Recently web-based educational systems collect vast amounts of data on user patterns, and data mining methods can be applied to these databases to discover interesting associations based on students' features and the actions taken by students in solving homework and exam problems. The main purpose of data mining is to discover the unobvious relationships among the data points within given data sets. Classification has emerged as a popular data mining task to find a model for grouping the data points based on extracted features of the training samples. This paper proposes a model for feature importance mining within a web-based educational system and represents an approach for classifying students in order to predict their final grades based on features extracted from logged data in the online educational system. A combination of multiple classifiers leads to significant improvement in classification performance. By weighing feature vectors representing feature importance using a Genetic Algorithm we can optimize the prediction accuracy and obtain significant improvement over raw classification. This approach is easily adaptable to different types of online courses, different population sizes, and allows for different features to be analyzed.

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