What is the level of accuracy can be achieve in predicting student performances ???

With the recent developments in the Internet allowed many leading educational institutions to offer online teaching and learning through Learning Management Systems. Systems with different capabilities and approaches have been developed to deliver online education which makes the communication channel between student and teacher into a much more virtual one.
 

The most important consideration would be to identify the student’s performance accurately and provide the necessary support to the students to improve their knowledge levels. But the main problem of measuring the students in accurately and to classify them correctly in to group to predict their performance levels is a huge challenge.

Behrouz Minaei-Bidgoli, Deborah A. Kashy, Gerd Kortemeyer, William F. Punch [2003] have researched on applying data mining methodologies to classify the students in to different groups and try to predict their performance achievement for the future.

For this research they used Quadratic Bayesian classifier, 1-nearest neighbor (1-NN), k-nearest neighbor (k-NN), Parzen-window, multilayer perceptron (MLP), and Decision Tree as the data mining mechanisms and by combining multiple classifiers they hoped to improve classifier performance. For the decision trees they have used C5.0, CART, QUEST, CRUISE algorithms.

In this paper they focused on using a Genetic Algorithm to optimize a combination of classifiers. They used GAToolBox for MATLAB to implement a Genetic Algorithm to optimize classification performance and to find a population of best weights for every feature vector which minimize the classification error rate.

Finally they concluded that using Genetic Algorithm more than a 10% performance improvement can be achieved and having the information generated the instructor would be able to identify students at risk early.

Reference : B. Minaei-Bidgoli, D. A. Kashy, G. Kortmeyer, and W. F. Punch, “Predicting student performance: an application of data mining methods with an educational web-based system,” in Frontiers in Education, 2003. FIE 2003 33rd Annual, 2003, vol. 1, p. T2A–13.