Semantics-Aware Context-Based Learner Modelling Using Normalized PSO for Personalized E-learning

Abstract

E-learning proves its importance in the diverse educational levels over traditional education. An adaptive e-learning system needs to deduce the learner model for adding personalization to instructional websites. The learner model is the perception repository about the e-content user, which can be inferred implicitly by employing meaningful semantic analysis of the text. In this research, a novel methodology is proposed to conceptually deduce the semantic learner model for personalized e-learning recommendations. Firstly, Conceptual Learner Model (CLM) is developed based on the learner’s behavior and context-based text semantic representation by exploiting concepts from the ConceptNet knowledge base, with a significant association of patterns and rules. Then, Expanded Contextual Learner Model (ECLM) is developed by exploring the latent semantics in graphs to add concepts with the common-sense meanings that exceeded the named entities. The learner’s knowledge graph is defined based on contextually associated concepts. Semantic relations in ConceptNet are exploited to extend learner models. The Normalized Particle Swarm Optimization (NPSO) algorithm is used to learn the importance of the relation types between the concepts. Thus, CLM and ECLM each are represented as a vector of weighted concepts in which updating is obtained automatically. The proposed recommendation system incorporates dynamic learner models to predict an appropriate e-content with the highest ranking, matching the true needs of a particular learner. Our simulation results show that the performance of ECLM is better Mean Reciprocal Rank (MRR) value 0.780 than other existing methods.