ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.017966 Article Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning Saurabh Pal 1 , Pijush Kanti Dutta Pramanik 1 , Musleh Alsulami 2 , Anand Nayyar 3, * , Mohammad Zarour 4 and Prasenjit Choudhury 1 1 Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India 2 Department of Information Systems, Umm Al-Qura University, Makkah, KSA 3 Graduate School, Duy Tan University, Da Nang, Vietnam 4 Prince Sultan University, Riyadh, Saudi Arabia * Corresponding Author: Anand Nayyar. Email: anandnayyar@duytan.edu.vn Received: 19 February 2021; Accepted: 13 April 2021 Abstract: 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 identifed 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 identifed contexts and fnd the interdependency among them. The acquiring meth- ods of these contexts are also defned. 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 identifed to make a personalized recommendation to a learner. Keywords: Personalized e-learning; DEMATEL; learner model; ontology; learner context; personalized recommendation; adaptive decisions 1 Introduction The availability of information over the Internet has made learning easier and unlocked different ways of learning [1]. However, recommendation of learning ftting to a learner’s learn- ing suitability and requirement remains lacking. Each Learner is different, in terms of various factors such as knowledge, demographics, environment, situation, difference in learning adeptness, requirements, etc. Accordingly, each learner’s acceptability of the information available on the web is unique. Different situational conditions, educational backgrounds, and cognitive settings do not allow learners to uniformly accept the information or learning material available on the Inter- net [2]. Arbitrarily overloading learners with information often causes frustration and confusion This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.