Semantics aware intelligent framework for content-based e-learning recommendation

Abstract

E-learning accounts for the emergence of re-skilling, up-skilling, and augmenting the traditional education system by providing knowledge delivery. The meaningful learning approach is based on a constructivist process for conceptually modeling an individual’s current and past knowledge or experience towards personalization. This research proposes a novel framework in which semantic analysis of e-content is combined with deep machine learning techniques into an e-learning Intelligent Content-Based Recommendation System (ICRS), with the goal of assisting learners in selecting appropriate e-learning materials. The system tackles textual e-content to extract representative terms and their mutual semantic relationships by which a structure of the context-based graph is developed. Thus, the e-content is semantically represented according to the learner’s terms that are expanded using the ConceptNet semantic network to represent the textual knowledge meaningfully. Furthermore, a new approach is proposed wherein more contextual and semantic information among concepts with graphs are combined to infer the relative semantic relations between terms and e-learning resources in order to build the semantic matrix. This new approach is utilized to generate learners’ semantic datasets used for the classification of available resources to enrich the recommendation. Here, four machine learning (ML) models and an augmented deep learning (DL) recommender model named LSTMM, which are developed for the ICRS framework. The models have been evaluated and compared using a user sequential semantic dataset. Our results show that the LSTMM performs better than others in terms of Accuracy and F1 Score of 0.8453 and 0.7731 respectively.