Smart learning using personalised recommendations in web-based learning systems using artificial bee colony algorithm to improve learning performance

Abstract

Many of e-learning systems in their web-based courses do not have personalisation based on individual needs and their capabilities. Main challenging aspect of personalised delivery of e-learning is concerned with an adaptive course delivery along with content delivery. Personalised e-learning environment provide recommendations to learning community for supporting and also helping them go through the process of e-learning, as it plays a crucial role in promotion of smart learning in smart cities. In this work, a novel framework namely, personalised bee recommender for e-learning (PBReL) based on artificial bee colony (ABC) optimisation is proposed to build a structure of recommendation by using K-means clustering. Many other recommender system are available that made use of ABC to identify its optimal learning path. Experiments are carried out by using web links and contents of Moodle-based learning management system (LMS). Results show that the proposed framework obtains higher precision and coverage.