Semantics-Aware Context-Based Learner Modelling Using Normalized PSO for Personalized E-learning

Authors

  • Hadi Ezaldeen Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Sukant Kishoro Bisoy Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Rachita Misra Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Rawaa Alatrash Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2148

Keywords:

Personalized E-learning Recommendation; Contextual Learner Model; Semantic Analysis; Knowledge Graph; Normalized PSO; Prefix Tree.

Abstract

E-learning proves its importance in the diverse educational levels over traditional education. An adaptive e-learning system needs to deduce the learner model for adding personalization to instructional websites. The learner model is the perception repository about the e-content user, which can be inferred implicitly by employing meaningful semantic analysis of the text. In this research, a novel methodology is proposed to conceptually deduce the semantic learner model for personalized e-learning recommendations. Firstly, Conceptual Learner Model (CLM) is developed based on the learner’s behavior and context-based text semantic representation by exploiting concepts from the ConceptNet knowledge base, with a significant association of patterns and rules. Then, Expanded Contextual Learner Model (ECLM) is developed by exploring the latent semantics in graphs to add concepts with the common-sense meanings that exceeded the named entities. The learner’s knowledge graph is defined based on contextually associated concepts. Semantic relations in ConceptNet are exploited to extend learner models. The Normalized Particle Swarm Optimization (NPSO) algorithm is used to learn the importance of the relation types between the concepts. Thus, CLM and ECLM each are represented as a vector of weighted concepts in which updating is obtained automatically. The proposed recommendation system incorporates dynamic learner models to predict an appropriate e-content with the highest ranking, matching the true needs of a particular learner. Our simulation results show that the performance of ECLM is better Mean Reciprocal Rank (MRR) value 0.780 than other existing methods.

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Author Biographies

Hadi Ezaldeen, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India

Hadi Ezaldeen is a Ph.D. in Computer Science and Engineering (CSE) from C.V. Raman Global University, India, in 2022. He received his Master’s degree in Web Science (MWS) from the Syrian Virtual University (SVU), Damascus, Syria. He has worked as a tutor (Assistant Professor), and an assistant supervisor of several graduation projects in the Faculty of Information Technology Engineering (ITE), at SVU. His research areas include Machine Learning, Semantic Analysis, Recommendation Systems, Natural Language Processing, and Text Mining. He has publications in reputed Journals and International Conferences indexed in SCI and Scopus. He has achieved interesting projects in the field of Web Intelligence and Knowledge Discovery.

Sukant Kishoro Bisoy, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India

Sukant Kishoro Bisoy received Ph.D., M.Tech, Bachelor’s degree in Computer Engineering in 2017, 2003 and 2000, respectively. He is working as Associate Professor in CSE, C. V. Raman Global University, India. His current research interests are on Neuro-robotics, Machine Learning, Cloud computing, Software Defined Network. He has been involved in organizing many conferences such IEEE ICICRS 2019, IEEE ANTS 2017, ICACIE-2016 (SPRINGER), ICHPCA-2014 (IEEE), many Workshop/FDP. He has several publications in Journals and Conferences indexed in SCI and Scopus. He has published patent. Reviewed papers in journals like ELSEVIER, SPRINGER, WILEY and INDERSCIENCE.

Rachita Misra, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India

Rachita Misra, retired Professor and Head of Department of Computer Science and Information Technology at C.V. Raman Global University, is a Ph.D. from the IIT Kharagpur, India. She has more than 40 years of experience in teaching, research, and IT industry. She has more than 50 scholarly publications to her credit, in international journals, conference proceedings, and book chapters in the areas of digital image processing, computational intelligence, and parallel processing. She is a senior member of IEEE and a life member of CSI, OITS, AIPRUI, and ISCA.

Rawaa Alatrash, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India

Rawaa Alatrash is a Ph.D. scholar in Computer Science and Engineering at C.V. Raman Global University (CGU), India. She received her Master’s degree in Computer Science and Engineering from CGU. She is presently working as an Assistant Professor at CGU, teaching in the field of Computer Science and Information Technology. Her main research interests include Deep Learning, Knowledge-based Systems, Natural Language Processing, and Text Mining. She has publications in reputed Journals and International Conferences indexed in SCI and Scopus. She is a web developer. She has done many remarkable projects in Web Intelligence and Sentiment Analysis. Reviewed papers in journals like River Publishers and Science Publishing Group.

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Published

2022-04-16

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