Title: Semantically enhanced machine learning approach to recommend e-learning content

Authors: Hadi Ezaldeen; Rachita Misra; Rawaa Alatrash; Rojalina Priyadarshini

Addresses: Department of Computer Science and Engineering, C.V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar – 752054, Odisha, India ' Department of Computer Science and Information Technology, C.V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar – 752054, Odisha, India ' Department of Computer Science and Engineering, C.V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar – 752054, Odisha, India ' Department of Computer Science and Information Technology, C.V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar – 752054, Odisha, India

Abstract: E-learning platforms contain a large number of heterogeneous resources of knowledge. In current e-learning systems, learners spend a lot of time and effort trying to find relevant learning resources. It is necessary to consider the learner's true needs according to different factors, such as learning style, experience, and preferences. Learning needs to be relevant to the context of the required concept. This work presents analytical review of the current status of e-learning system design. A new approach based on machine learning (ML) combined with ontology techniques has been suggested to develop an efficient e-learning recommender system. The proposed model uses Bayesian inference algorithm to predict learning materials, which are collected and indexed within the system. Ontology has been used to expand the initial terms extracted. Semantic relation between the learning material and the term was generated and fed to the model. Experimental results using this model show promising performance.

Keywords: e-learning system; ontology; machine learning; recommender system; Bayesian inference; semantic relation; DBpedia; WordNet.

DOI: 10.1504/IJEB.2020.111095

International Journal of Electronic Business, 2020 Vol.15 No.4, pp.389 - 413

Received: 16 Mar 2020
Accepted: 19 Jun 2020
Published online: 09 Nov 2020 *

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