Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning, personalization and ubiquity have become important aspects of online learning. To make learning more personalized and ubiquitous, we propose a learner model for a query-based personalized learning recommendation system. Several contextual attributes characterize a learner, but considering all of them is costly for a ubiquitous learning system. In this paper, a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling. A total of 208 students are surveyed. DEMATEL (Decision Making Trial and Evaluation Laboratory) technique is used to establish the validity and importance of the identified contexts and find the interdependency among them. The acquiring methods of these contexts are also defined. On the basis of these contexts, the learner model is designed. A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system. In a ubiquitous learning scenario, the necessary adaptive decisions are identified to make a personalized recommendation to a learner.