How we can enhance the performance of the students???

Web based online learning systems are widely used around the world to provide various types of educational programs. With the increase in the networking and the use of Internet resources enables these educational programs to be offered through web based learning management systems and several web based educational systems with various functionalities have been developed to deliver online education more effectively to the students.

 

Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F. Punch [2004] have tried to research on classifying the students in to different group and predict the final grade that they will achieve in the end of the course modules. This research is based on the web based learning management systems which is known as Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA) developed at Michigan State University.

For this research they concerned on the three kinds of large data sets available in the LON-CAPA which can be categorized as educational resources, information about users and the activity log databases which log actions taken by students.  

For this study they restricted four different classifiers using the LON-CAPA dataset which were Quadratic Bayesian classifier, 1-nearest neighbor (1-NN), k-nearest neighbor (k-NN), Parzen-window. The approach they used in this research is to combine the classifiers together to increase the performance of the classifiers and this is known as online classification fusion.

Another important aspect they highlighted in this research is the use of Genetic Algorithms as an optimization tool for resetting the parameters in other classifiers. The main idea was to use the Genetic Algorithms to find a population of best weights for every feature vector, which minimize the classification error rate.

As the conclusions they mentioned that a combination of multiple classifiers leads to a significant accuracy improvement in the given data sets. Weighing the features and using a genetic algorithm to minimize the error rate improves the prediction accuracy by at least 10% test cases as well. Finally they suggested that use of these algorithms as tools can be used to identify those students who are at risk in very large classes and it will help the instructors to provide appropriate advising in more effective manner.

Reference: B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, “Enhancing online learning performance: an application of data mining methods,” in The 7th IASTED International Conference on Computers and Advanced Technology in Education (CATE 2004), 2004, pp. 173–178.