Developing Student Model using Kohonen Network in Adaptive Hypermedia Learning System

This paper presents a study on method to develop student model by identifying the students' characteristics in an adaptive hypermedia learning system. The study involves the use of student profiling techniques to identify the features that may be useful to help the researchers have a better understanding of the student in an adaptive learning environment. We propose a supervised Kohonen network with hexagonal lattice structure to classify the student into 3 categories: beginner, intermediate and advance to represent their knowledge level while using the learning system. An experiment is conducted to see the proposed Kohonen network's performances compared to the other types of Kohonen networks in term of learning algorithm and map structure. 10-fold cross validation method is used to validate the network performances. Results from the experiment shows that the proposed Kohonen network produces an average percentage of accuracy, 81.3889% in classifying the simulated data and 51.6129% when applied to the real student data.