A Personalised Recommendation Framework for Ubiquitous Learning System

The traditional e-learning has been developed into personalised and ubiquitous learning, in which the learners find learning materials (LMs) that are suitable to their contextual requirements, and can access them from anywhere and anytime. In this paper, we propose a framework for a personalised recommendation in a ubiquitous learning platform, following a knowledge-based approach. The framework comprises modules like query processing, information storage and retrieval, and learner context mapping and reasoning. Learner's implicit and explicit contexts are used for assessing the preference and suitability and mapping with the LMs that are retrieved based on the learner's query analysis, with the help of educational metadata. Selecting suitable LMs based on different factors is a multi-criteria decision making (MCDM) problem. For prioritising the selection factors, we use SWARA, and for multi-objective decision making, we apply MOORA. Utilising these two techniques, the LMs are ranked and are recommended accordingly.