Using Clustering or Association rules on Educational Data

In any learning management system or e-learning environment student accessibility to the study materials are varying in a very diverse manner. Many student are expecting study materials which can be easily understandable so that they can gather more knowledge through them. In order to provide the expected level of learning for the students identifying the learning behavior of each student is required. Due to this nature of different learning patterns of each student, teachers or tutors should be able to provide the materials much more pervasive manner. 

J. Mamcenko, I. Sileikiene, J. Lieponiene, R. Kulvietiene [2011] have tried to apply data mining methodologies to discover the different learning patterns of each students. In their approach they used clustering and association rule mechanisms to identify the learning patterns. 

For the research they focused their research on a programming course examination and the ways that the students have answered. Their collected data on questions given for the student and the answers given by the students with the amount of time that each student has spent on each question to construct the dataset. For the clustering mechanism they used Kohonen algorithm which is based on a self-organizing map (SOM) or self-organizing feature map (SOFM) that is a type of artificial neural network (ANN) and for the association rule mining they used Simultaneous Depth-First Expansion  (SIDE) algorithm.

The analysis of this research indicated that the students find more difficulties in understanding the course development policies. Another indication they found is that the student who are spending more time on questions answered them incorrectly than the questioned they answered in lesser time.

According to their research it can be concluded that the clustering mechanism they used can be used to discover the statistical information about the student behaviors and the learning patterns and the association rules are better way of identifying the complex rules behind student behaviors on e - examination data.   

Reference : J. Mamcenko, I. Sileikiene, J. Lieponiene, R. Kulvietiene, Analysis of E-Exam Data Using Data Mining Techniques, 2011