Ramneet Kaura, Deepali Guptaa,*, and Mani Madhukarb
| 1. Ashraf E., Manickam S., andKaruppayah S.A comprehensive review of course recommender systems in E-Learning. Journal of Educators Online, vol. 18, no. 1, 2021.
2. Bhaskaran S., Marappan R., andSanthi B.Design and analysis of a cluster-based intelligent hybrid recommendation system for e-learning applications. Mathematics, vol. 9, no. 2, pp. 197, 2021.
3. Dahdouh K., Dakkak A., Oughdir L., andIbriz A.Large-scale e-learning recommender system based on Spark and Hadoop. Journal of Big Data, vol. 6, no. 1, pp.1-23, 2019.
4. Eckerdal A., Kinnunen P., Thota N., Nylén A., Sheard J. and Malmi L. Teaching and learning with MOOCs: Computing academics' perspectives and engagement. In Proceedings of the2014 conference on Innovation & technology in computer science education, pp. 9-14, 2014, June.
5. Ezaldeen H., Misra R., Bisoy S.K., Alatrash R., andPriyadarshini R.A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis. Journal of Web Semantics, vol. 72, pp. 100700, 2022.
6. Ghosh S., Roy S., andSen, S. An efficient recommendation system on e-learning platform by query lattice optimization. In Data Management, Analytics and Innovation: Proceedings of ICDMAI2020, Volume 1, Springer Singapore, pp. 73-86, 2021.
7. Gomede E., de Barros, R.M. and de Souza Mendes, L. Deep auto encoders to adaptive E-learning recommender system. Computers and education: Artificial intelligence, vol. 2, pp. 100009, 2021.
8. Joy, J. and Renumol, V.G. Comparison of generic similarity measures in E-learning content recommender system in cold-start condition. In2020 IEEE Bombay section signature conference (IBSSC), IEEE, pp. 175-179, 2020, December.
9. Khanal, S.S., Prasad, P.W.C., Alsadoon, A., and Maag, A. A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, vol. 25, pp.2635-2664, 2020.
10. Madhavi A., Nagesh A., andGovardhan A.A Study on E-Learning and Recommendation System. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), vol. 15, no. 5, pp.748-764, 2022.
11. Mawane J., Naji A., andRamdani M.Unsupervised deep collaborative filtering recommender system for e-learning platforms. In Smart Applications and Data Analysis: Third International Conference, SADASC 2020, Marrakesh, Morocco, June 25-26, 2020, Proceedings 3, Springer International Publishing, pp. 146-161, 2020.
12. Nafea S.M., Siewe F., andHe Y.On recommendation of learning objects using felder-silverman learning style model. IEEE Access, vol. 7, pp.163034-163048, 2019.
13. Prabhakar S., Spanakis G., andZaiane O.Reciprocal recommender system for learners in massive open online courses (MOOCs). In Advances in Web-Based Learning-ICWL 2017: 16th International Conference, Cape Town, South Africa, September 20-22, 2017, Proceedings 16, Springer International Publishing, pp. 157-167, 2017.
14. Qomariyah, N.N. and Fajar, A.N. Recommender system for e-learning based on personal learning style. In2019 international seminar on research of information technology and intelligent systems (ISRITI), IEEE, pp. 563-567, 2019, December.
15. Souabi S., Retbi A., Idrissi M.K.I.K., and Bennani, S. Recommendation Systems on E-Learning and Social Learning: A Systematic Review. Electronic Journal of E-Learning, vol. 19, no. 5, pp. 432-451, 2021.
16. Tarus J.K., Niu Z., andMustafa G.Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial intelligence review, vol. 50, pp.21-48, 2018.
17. Wan, S. and Niu, Z.An e-learning recommendation approach based on the self-organization of learning resource. Knowledge-Based Systems, vol. 160, pp.71-87, 2018.
18. Wan, S. and Niu, Z.A hybrid e-learning recommendation approach based on learners' influence propagation. IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 5, pp.827-840, 2019.
19. Zhang Q., Lu J., andZhang G.Recommender Systems in E-learning. Journal of Smart Environments and Green Computing, vol. 1, no. 2, pp.76-89, 2021.
20. Fernández-García, A.J., Iribarne, L., Corral, A., Criado, J., and Wang, J.Z. A recommender system for component-based applications using machine learning techniques. Knowledge-Based Systems, vol. 164, pp.68-84, 2019.
21. George, G. and Lal, A.M.Review of ontology-based recommender systems in e-learning. Computers & Education, vol. 142, pp. 103642, 2019.
22. Santos D.O., Durelli V.H., Endo A.T. and Eler M.M.Evaluating Random Input Generation Strategies for Accessibility Testing. In ICEIS (2), pp. 66-75, 2021.
23. Assami S., Daoudi N., andAjhoun R. Personalization criteria for enhancing learner engagement in MOOC platforms. In2018 IEEE Global Engineering Education Conference (EDUCON), IEEE, pp. 1265-1272, 2018, April.
24. Dahdouh K., Oughdir L., Dakkak A., andIbriz A. Smart courses recommender system for online learning platform. In2018 IEEE 5th International Congress on Information Science and Technology (CiSt), IEEE, pp. 328-333, 2018, October.
25. Prabhakar S., Spanakis G., andZaiane O.Reciprocal recommender system for learners in massive open online courses (MOOCs). In Advances in Web-Based Learning-ICWL 2017: 16th International Conference, Cape Town, South Africa, September 20-22, 2017, Proceedings 16, Springer International Publishing, pp. 157-167, 2017.
26. Dwivedi, S. and Roshni, V.K. Recommender system for big data in education. In2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH), IEEE, pp. 1-4, 2017, August.
27. Chen G., Davis D., Lin J., Hauff C., andHouben G.J.Beyond the MOOC platform: gaining insights about learners from the social web. In Proceedings of the 8th ACM Conference on Web Science, pp. 15-24, 2016, May.
28. Conache M., Dima R., andMutu A.A comparative analysis of MOOC (Massive Open Online Course) platforms. Informatica Economica, vol. 20, no. 2, 2016.
29. Iniesto, F. and Rodrigo, C.Accessible user profile modeling for academic services based on MOOCs. In Proceedings of the XVI International Conference on Human Computer Interaction, pp. 1-2, 2015, September.
30. Gupta, D. and Madhukar, M. Operational Challenges in Online Self-Learning Education Adoption. In2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE, pp. 51-55, 2021, October.
31. Gupta, D. and Madhukar, M. Bibliometric Analysis of MOOC using Bibliometrix Package of R. In2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, pp. 157-161, 2020, December.
32. Kaur R., Gupta D., Madhukar M., Singh A., Abdelhaq M., Alsaqour R., Breñosa J., andGoyal N.E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation. Electronics, vol. 11, no. 20, pp. 3354, 2022.
33. Shamas S., Panda S.N., andSharma, I. Review on Lung Nodule Segmentation-Based Lung Cancer Classification Using Machine Learning Approaches. In Artificial Intelligence on Medical Data: Proceedings of International Symposium, ISCMM2021, Singapore: Springer Nature Singapore, pp. 277-286, 2022, July.
34. Goel S., Guleria K., andPanda, S.N. Anomaly based Intrusion Detection Model using Supervised Machine Learning Techniques. In2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), IEEE, pp. 1-5, 2022, October.
35. Kumar D., Kukreja V., Kadyan V., andMittal M.Detection of DoS attacks using machine learning techniques. International Journal of Vehicle Autonomous Systems, vol.15, no. 3-4, pp. 256-270, 2020.
36. Kaur A., Singh G., Kukreja V., Sharma S., Singh S., andYoon B.Adaptation of IoT with blockchain in Food Supply Chain Management: An analysis-based review in development, benefits and potential applications. Sensors, vol. 22, no. 21, pp. 8174, 2022.
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