Recommendation System Based on Video Processing in an E-Learning Platform

Manar Joundy Hazar, Mohsen Maraoui, Mounir Zrigui

Abstract

With online learning technology fatly growing, especially with the Сovid-19 pandemic, learning resources are produced in massive amounts, with high heterogeneity, and in numerous media formats. The key issue for today’s learners is how to access the required learning resource based on their preferences and skills? Learning videos have become the central role in e-learning of higher education institutions. As a learning content, videos prove it is an important and necessary content delivery tool in all online platforms such as online, flipped, and blended classes. Hence, indicating suitable videos in seminal years possibly will help to do research in a better way. In this article, we present a recommender system that will suggest and guide learners in choosing appropriate learning videos per their requirements. Our system is based on collective intelligence. Indeed, we analyze the comments of Internet users on the videos to extract their opinions and then compare them with the evaluations to obtain a better recommendation.

 

Keywords: recommender system, e-learning, learning video.

 

https://doi.org/10.55463/issn.1674-2974.49.6.6


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HAZAR M. J., TOMAN Z. H., and TOMAN S. H. Automated Scoring for Essay Questions in E- learning. Journal of Physics: Conference Series, 2019, 1294(4): 042014. https://doi.org/10.1088/1742-6596/1294/4/042014

SIMAMORA R. M. The Challenges of Online Learning during the COVID-19 Pandemic: An Essay Analysis of Performing Arts Education Students. Studies in Learning and Teaching, 2020, 1(2): 86–103. https://doi.org/10.46627/silet.v1i2.38

ZUBKOV A. D. MOOCs in Blended English Teaching and Learning for Students of Technical Curricula. In: ANIKINA Z. (ed.) Integrating Engineering Education and Humanities for Global Intercultural Perspectives. IEEHGIP 2022. Lecture Notes in Networks and Systems, Vol. 131. Springer, Cham, 2020: 539–546. https://doi.org/10.1007/978-3-030- 47415-7_57

RICCI F., ROKACH L., SHAPira B., and KANTOR P. B. Introduction to Recommender Systems Handbook. In: RICCI F., ROKACH L., SHAPIRA B., and KANTOR P. B. (eds.) Recommender Systems Handbook. Springer, Berlin, 2011: 1-35. http://dx.doi.org/10.1007/978-0-387-85820-3_1

SALEEM A. N., NOORI N. M., and OZDAMLI F. Gamification Applications in E-Learning: A Literature Review. Technology, Knowledge and Learning, 2022, 27(1): 139–159. https://doi.org/10.1007/s10758-020-09487-x

DEMETRIADIS S. N., KARAKOSTAS A., TSIATSOS T., CABALLÉ S., DIMITRIADIS Y. A., WEINBERGER A., PAPADOPOULOS P. M., PALAIGEORGIOU G., TSIMPANIS C., and HODGES M. Towards Integrating Conversational Agents and Learning Analytics in MOOCs. In: BAROLLI L., XHAFA F., JAVAID N., SPAHO E., and KOLICI V. (eds.) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, Vol. 17. Springer, Cham, 2018: 1061–1072. https://doi.org/10.1007/978-3-319-75928-9_98

CHANAA A., & EL FADDOULI N. E. Context- aware factorization machine for recommendation in Massive Open Online Courses (MOOCs). Proceedings of the International Conference on Wireless Technologies, Embedded and Intelligent Systems, Fez, 2019, pp. 1-6. https://doi.org/10.1109/WITS.2019.8723670

YU Z. The effect of teacher presence in videos on intrinsic cognitive loads and academic achievements. Innovations in Education and Teaching International, 2021. https://doi.org/10.1080/14703297.2021.1889394

NASULEA C., & NASULEA D. F. Teaching Economics in the Cloud: Assessing the Efficiency of Online Economics Teaching Methods. Proceedings of the 13th International Conference on Education and New Learning Technologies, 2021, pp. 9932-9941. https://dx.doi.org/10.21125/edulearn.2021.2032

RAZA S., & DING C. Progress in context-aware recommender systems — An overview. Computer Science Review, 2019, 31: 84–97. https://doi.org/10.1016/J.COSREV.2019.01.001

BURKE R. Hybrid Web Recommender Systems. In: BRUSILOVSKY P., KOBSA A., and NEJDL W. (eds.) The Adaptive Web. Lecture Notes in Computer Science, Vol. 4321. Springer, Berlin, Heidelberg, 2007: 377–408. https://doi.org/10.1007/978-3-540-72079-9_12

JANNACH D., ZANKER M., FELFERNIG A., and FRIEDRICH G. An introduction to recommender systems. Cambridge University Press, 2011.

ADOMAVICIUS G., & TUZHILIN A. Context-Aware Recommender Systems. In: RICCI F., ROKACH L., SHAPIRA B., and KANTOR P. (eds.) Recommender Systems Handbook. Springer, Boston, Massachusetts, 2011: 217–253. https://doi.org/10.1007/978-0-387-85820-3_7

AZADJALAL M. M., MORADI P., ABDOLLAHPOURI A., and JALILI M. A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Systems, 2017, 116: 130-143. https://doi.org/10.1016/j.knosys.2016.10.025

PERUMAL S. P., SANNASI G., and ARPUTHARAJ K. An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests. The Journal of Supercomputing, 2019, 75(8): 5145–5160. https://doi.org/10.1007/s11227-019-02791-z

GUO L., WEN Y., and LIU F. Location perspective-based neighborhood-aware POI recommendation in location-based social networks. Soft Computing, 2019, 23(22): 11935–11945. https://doi.org/10.1007/S00500-018-03748-9

TARUS J. K., NIU Z., and MUSTAFA G. Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 2018, 50(1): 21–48. https://doi.org/10.1007/s10462-017-9539-5

SHANG S., HUI Y., HUI P., CUFF P., and KULKARNI S. Beyond personalization and anonymity: Towards a group-based recommender system. Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, 2014, pp. 266–273. https://doi.org/10.1145/2554850.2554924

BOURKOUKOU O., EL BACHARI E., and EL ADNANI M. A Recommender Model in E-learning Environment. Arabian Journal for Science and Engineering, 2017, 42(2): 607–617. https://doi.org/10.1007/s13369-016-2292-2

HAJRI H., BOURDA Y., and POPINEAU F. A system to recommend open educational resources during an online course. Proceedings of the 10th International Conference on Computer Supported Education, Vol. 1, Funchal, 2018, pp. 99–109. https://doi.org/10.5220/0006697000990109

OKOYE I., MAULL K., FOSTER J., and SUMNER T. Educational recommendation in an informal intentional learning system. In: SANTOS O. C., & BOTICARIO J. G. Educational Recommender Systems and Technologies: Practices and Challenges. IGI Global, Hershey, Pennsylvania, 2012: 1–23. https://doi.org/10.4018/978-1-61350-489-5.ch001

DING L., LIU B., and TAO Q. Hybrid filtering recommendation in e-learning environment. Proceedings of the 2nd International Workshop on Education Technology and Computer Science, Vol. 3, Wuhan, 2010, pp. 177–180. https://doi.org/10.1109/ETCS.2010.378

MEDDEB O., MARAOUI M., and ZRIGUI M. Personalized Smart Learning Recommendation System for Arabic Users in Smart Campus. International Journal of Web-Based Learning and Teaching Technologies, 2021, 16(6): 1–21. https://doi.org/10.4018/IJWLTT.20211101.OA9

XU W., & ZHOU Y. Course video recommendation with multimodal information in online learning platforms: A deep learning framework. British Journal of Educational Technology, 2020, 51(5): 1734–1747. https://doi.org/10.1111/bjet.12951

NAJAFABADI M. K., MAHRIN M. N., CHUPRAT S., and SARKAN H. M. Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Computers in Human Behavior, 2017, 67: 113–128. https://doi.org/10.1016/J.CHB.2016.11.010

TREUDE C., SICARD M., KLOCKE M., and ROBILLARD M. TaskNav: Task-Based Navigation of Software Documentation. Proceedings of the IEEE/ACM 37th IEEE International Conference on Software Engineering, Florence, 2015, pp. 649–652. https://doi.org/10.1109/ICSE.2015.214

MAHMOUD A., & ZRIGUI M. Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic. The International Arab Journal of Information Technology, 2021, 18(1): 1-7. https://doi.org/10.34028/iajit/18/1/1

VASWANI A., SHAZEER N. M., PARMAR N., USZKOREIT J., JONES L., GOMEZ A. N., KAISER L., and POLOSUKHIN I. Attention Is All You Need, 2017. https://arxiv.org/pdf/1706.03762.pdf


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