Parliamentary Optimization to Build Personalized Learning Paths: Case Study in Web Engineering Curriculum

摘要

In this paper we present a practical application of POA (Parliamentary Optimization Algorithm) for creating personalized learning paths in online learning. The objective of building a personalized learning path is to produce a suitable sequence of learning units for a student to work with. We present and tune the parliamentary metaheuristic for a practical instance of the sequencing problem in a web engineering master programme and compare it with standard versions of other well established metaheuristics (PSO and genetic algorithms). Results suggest that permut-POA deals satisfactorily with sequencing problems and it is easy to fine tune, and also that it outperforms the other optimizers.