An e-Learning Recommendation System Framework

Wan-Er Kong (1), Su-Cheng Haw (2), Naveen Palanichamy (3), Siti Husna Abdul Rahman (4)
(1) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(2) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(3) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(4) Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
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How to cite (IJASEIT) :
Kong, Wan-Er, et al. “An E-Learning Recommendation System Framework”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 1, Feb. 2024, pp. 10-19, doi:10.18517/ijaseit.14.1.19043.
With the emergence of the digital era, the e-learning platform has become an effective tool for obtaining quality e-learning content. However, despite its potential, the true extent of its capabilities has yet to be fully explored. In order to attract users and maximize revenue, e-learning platforms are now expected to provide content tailored to their users' needs and preferences. These recommendations are generated by considering factors such as prior purchases, browsing history, demographic information, and more. By leveraging these advanced technologies, e-learning platforms can enhance the learning experience by providing users with content that is both engaging and relevant to their individual needs and interests. This paper explores the popular Machine Learning (ML) techniques employed in e-learning content recommender platforms. Two machine learning techniques, k-Nearest Neighbour Baseline (KNNBaseline) and Singular Value Decomposition (SVD), are selected and used to accurately forecast customer interests and preferences. By examining the data patterns and user behaviors, these ML techniques provide insights into the most relevant and personalized educational content for individual users, enhancing their learning experience. The item ratings predicted are generated based on the underlying pattern in past ratings of users. The performance of applied approaches was assessed using several evaluation metrics, which include root mean square error and mean absolute error.

E. Dalal, P. Singh, "Comparative analysis of various recommendation systems", New Approaches for Multidimensional Signal Processing, Springer Singapore, pp. 187–194, 2021. doi:.10.1007/978-981-33-4676-5_14.

M. Srivastava, "YouTube and Movie Recommendation System Using Machine Learning", IEEE International Conference on Electronics and Renewable Systems, pp.1352-1356, 2023. doi:10.1109/ICEARS56392.2023.10084999

S.C. Haw, L.J. Chew, K. Ong, K.W. Ng, P. Naveen, E.A. Anaam, "Content-based Recommender System with Descriptive Analytics", Journal of System and Management Sciences, vol. 12(5), pp. 105-120, 2022. doi:10.33168/JSMS.2022.0507

R.K. Mishra, J.A.A Jothi, S. Urolagin, K. Irani, "Knowledge based topic retrieval for recommendations and tourism promotions", International Journal of Information Management Data Insights, vol. 3(1), 100145, 2023. doi:10.1016/j.jjimei.2022.100145

S.C. Haw, L.J. Chew, K.W. Ng, P. Naveen, "Ontology-based Recommender System with Descriptive Analytics in e-Commerce", IEEE International Conference on Big Data Engineering and Education, pp. 47-52, 2022. doi:10.1109/BDEE55929.2022.00015

C. Susaie, C.K. Tan, P.Y. Goh, Learning Experience with LearnwithEmma, Journal of Informatics and Web Engineering, vol. 1(2), pp. 30-44, 2022. doi:10.33093/jiwe.2022.1.2.3

A.A. Zaveri, R. Mashood, S. Shehmir, M. Parveen, N. Sami, M. Nazar, AIRA: An Intelligent Recommendation Agent Application for Movies, Journal of Informatics and Web Engineering, vol. 2(2), pp. 72-89, 2023. doi:10.33093/jiwe.2023.2.2.6

A. Peuker, T. Barton, "Recommendation Systems and the Use of Machine Learning Methods", Apply Data Science: Introduction, Applications and Projects, pp. 79-93. 2023.

D.T. Tran, J. H. Huh, J," New machine learning model based on the time factor for e-commerce recommendation systems", The Journal of Supercomputing, pp. 1-46, 2022. doi:10.1007/s11227-022-04909-2

S. Khademizadeh, Z. Nematollahi, F. Danesh, "Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques", Library & Information Science Research, vol. 44(4), 101191, 2022. doi:10.1016/j.lisr.2022.101191

Z. Jiang, L. Zhang, L. Zhang, W. Wen," Investor sentiment and machine learning: Predicting the price of China's crude oil futures market", Energy, vol. 247, 12347, 2022. doi:10.1016/

Ishwarappa, J. Anuradha, "A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology", Procedia Computer Science, vol. 48, pp. 319-324, 2015. doi:10.1016/j.procs.2015.04.188

R.J. Kuo, H.R. Cheng, "A content-based recommender system with consideration of repeat purchase behavior", Applied Soft Computing, vol. 127, 109361, 2022. doi:10.1016/j.asoc.2022.109361

R. Yin, K. Li, G. Zhang, J. Lu, "A deeper graph neural network for recommender systems", Knowledge-Based Systems, vol. 185,105020, 2019. doi:10.1016/j.knosys.2019.105020

Y. Deldjoo, M. Schedl, P. Cremonesi, G. Pasi, "Recommender Systems Leveraging Multimedia Content", ACM Computing Surveys, vol. 53(5), pp. 1–38, 2021. doi:10.1145/3407190

S. Wu, F. Sun, W. Zhang, X. Xie, B. Cui, "Graph Neural Networks in Recommender Systems: A Survey", ACM Computing Surveys, vol. 55(5), pp. 1–37, 2022. doi:10.1145/3535101

J.H. Zelaya, C. Porcel, J.B. Moreno, A.T. Lorente, E.H. Viedma, "New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests", Information Sciences, vol. 536, pp. 156-170, 2020. doi:10.1016/j.ins.2020.05.071

I.H. Sarker, A. Colman, J. Han, A.I. Khan, Y.B. Abushark, K. Salah, "BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model", Mobile Networks and Application, vol. 25, pp. 1151–1161, 2020. doi:10.1007/s11036-019-01443-z

K.K. Jena, S.K. Bhoi, T.K. Malik, K.S. Sahoo, N.Z. Jhanjhi, S. Bhatia, F. Amsaad, "E-Learning Course Recommender System Using Collaborative Filtering Models", Electronics, vol. 12(1), pp. 157, 2022. doi:10.3390/electronics12010157

A. Jeejoe, V. Harishiv, P. Venkatesh, S.K.B. Sangeetha, "Building a Recommender System Using Collaborative Filtering Algorithms and Analyzing its Performance", Advances in Science and Technology, vol. 124, pp. 478-485, 2023. doi:10.4028/p-1h18ig

L.J. Chew, S.C. Haw, S. Subramaniam, "A hybrid recommender system based on data enrichment on the ontology modelling", F1000Research, vol. 10, 937, 2021. doi:10.12688/f1000research.73060.1

S.M. Asaad, K.Z. Ghafoor, H. Sarhang, A. Mulahuwaish, "Point-of-Interests Recommendation Service in Location-Based Social Networks: A Survey, Research Challenges, and Future Perspectives", Sustainable Smart Cities: Theoretical Foundations and Practical Considerations, pp. 43-64, 2022. doi:10.1007/978-3-031-08815-5_4

F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, "Recommendation systems: Principles, methods and evaluation", Egyptian Informatics Journal, vol. 16(3), pp. 261–273, 2015. doi:10.1016/j.eij.2015.06.005

J. Wei, J. He, K. Chen, Y. Zhou, Z. Tang, "Collaborative filtering and deep learning based recommendation system for cold start items", Expert Systems with Applications, vol. 69, pp. 29-39, 2017. doi:10.1016/j.eswa.2016.09.040

J. Tarus, Z. Niu, B. Khadidja, "E-Learning Recommender System Based on Collaborative Filtering and Ontology", International Journal of Computer and Information Engineering, vol. 11(2), pp. 256-261, 2017. doi:10.5281/zenodo.1129067

J.K. Tarus, Z. Niu, D. Kalui, "A hybrid recommender system for e-learning based on context awareness and sequential pattern mining", Soft Computing, vol. 22, pp. 2449–2461, 2018. doi:10.1007/s00500-017-2720-6

S. Wan, Z. Niu, "A Hybrid E-Learning Recommendation Approach Based on Learners' Influence Propagation", IEEE Transactions on Knowledge and Data Engineering, vol. 32(5), pp. 827-840, 2019. doi:10.1109/TKDE.2019.2895033

H. Ezaldeen, M. Misra, S.K. Bisoy, R. Alatrash, R. Priyadarshini, "A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis", Journal of Web Semantics, vol. 72, 100700, 2022. doi:10.1016/j.websem.2021.100700

Z. Shahbazi, Y. Byun, "Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches", Mathematics, vol. 10(7), pp. 1192, 2022. doi:10.3390/math10071192

R. Alatrash, R. Priyadarshini, H. Ezaldeen, A. Alhinnawi, "Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation", Cognitive Systems Research, vol. 75, pp. 53-69, 2022. doi:10.1016/j.cogsys.2022.07.002

D. Pramod, P. Bafna, "Conversational recommender systems techniques, tools, acceptance, and adoption: A state of the art review", Expert Systems with Applications, 117539, 2022. doi:10.1016/j.eswa.2022.117539

M. Hafsa, P. Wattebled, J. Jacques, L. Jourdan, "Multi-objective recommender system for corporate MOOC", The Genetic and Evolutionary Computation Conference Companion, pp. 2314–2317, 2022. doi:10.1145/3520304.3534058

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