Sequencing educational contents using clustering and ant colony algorithms

Abstract

This work presents a model to optimize the presentation order of educational contents in the Moodle e-Learning platform. The objective here is to infer those learning paths for which it is expected the students may achieve the best performance. The foundations of the proposed model are (i) a clustering of similar students according to a student model, and (ii) a metaheuristic to obtain an improved educational content sequence for each group. The clustering of similar students is achieved by a modified k-prototypes algorithm. Then, for each group of students, an Ant Colony Optimization algorithm is used for self-organize the sequencing of the educational content that better adapts to its learning characteristics. Finally, to evaluate the proposal, synthetic and real data were used for testing purposes. Experimental results show the viability of the proposed approach.