Learner-Centric Hybrid Filtering-Based Recommender System for Massive Open Online Courses

doi:10.23940/ijpe.23.05.p4.324333

Abstract

Abstract: Massive Open Online Courses (MOOCs) have significantly impacted the basic education industry since 2012. Online platforms enable learners to connect with the instructors present worldwide and reduce learning time by approximately 50-60%. Many universities have opted for their survival in the pandemic of COVID-19. During the pandemic, novice learners were not able to enroll in the relevant courses on these platforms, and instructors also faced challenges to satisfy their learners' needs. Each online forum has its own recommender system, and these systems only recommend courses from their own platforms. As a result, these platforms fail to satisfy the learners' educational needs and thereby increase the dropout ratio. The main objective of this study is to create a single platform for learners to search for courses from multiple platforms like Coursera, Udemy, EdX, Udacity, etc., and then recommend courses according to the learning behavior of a learner. A user profile is created in three ways, i.e., by registering, uploading their CV, or through their LinkedIn accounts. The recommender system then uses this user profile as input and recommends the relevant courses for user adaption. In this paper, demographic, content-based and collaboration-based recommender systems are used for recommendations. To validate, multi-model filtering, namely random, user-based collaboration, item-based collaboration, and matrix factorization, is used to obtain the values of the performance metrics such as RMSE, precision, and recall. On the basis of the results, the best result is obtained from user-based collaboration filtering on 6,000 dimensions of the dataset. The value of RMSE in the case of user-based collaboration filtering is 0.101, the value of precision is 0.82, and the value of recall is 0.822. Thus, the learner-centric hybrid filtering-based recommender system for MOOC platforms is implemented to enhance user adaptation.

Key words: massive open online courses (MOOCs), e-learning, user profile, recommender system, content-based filtering, machine learning

Ramneet Kaur, Deepali Gupta, and Mani Madhukar. Learner-Centric Hybrid Filtering-Based Recommender System for Massive Open Online Courses [J]. Int J Performability Eng, 2023, 19(5): 324-333.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

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.

[1]	C. Rohith Bhat and Madhusundar Nelson. Artificial Intelligence Based Credit Card Fraud Detection for Online Transactions Optimized with Sparrow Search Algorithm [J]. Int J Performability Eng, 2023, 19(9): 624-632.
[2]	Savita Khurana, Gaurav Sharma, and Bhawna Sharma. Hybrid Machine Learning Model for Load Prediction in Cloud Environment [J]. Int J Performability Eng, 2023, 19(8): 507-515.
[3]	K. Eswara Rao, Bala Murali Pydi, T. Panduranga Vital, P. Annan Naidu, U. D. Prasann, and T. Ravikumar. An Advanced Machine Learning Approach for Student Placement Prediction and Analysis [J]. Int J Performability Eng, 2023, 19(8): 536-546.
[4]	Babaljeet Kaur and Shalli Rani. Are the Customers Receiving Exact Recommendations from the E-Commerce Companies? Towards the Identification of Gray Sheep Users Using Personality Parameters [J]. Int J Performability Eng, 2023, 19(7): 425-433.
[5]	Kshitij Kumar Sinha, Manoj Mathur, and Arun Sharma. Suitability Index Prediction for Residential Apartments Through Machine Learning [J]. Int J Performability Eng, 2023, 19(7): 434-442.
[6]	Manpreet Kaur and Shalli Rani. Recommender System: Towards Identification of Shilling Attacks in Rating System Using Machine Learning Algorithms [J]. Int J Performability Eng, 2023, 19(7): 443-451.
[7]	Srishti Bhugra and Puneet Goswami. Exploratory Review of Machine Learning-Based Software Component Reusability Prediction [J]. Int J Performability Eng, 2023, 19(7): 452-461.
[8]	Harsha Gaikwad, Sanil Gandhi, Arvind Kiwelekar, and Manjushree Laddha. Analyzing Brain Signals for Predicting Students’ Understanding of Online Learning: A Machine Learning Approach [J]. Int J Performability Eng, 2023, 19(7): 462-470.
[9]	Rakesh Kumar, Sunny Arora, Ashima Arya, Neha Kohli, Vaishali Arya, and Ekta Singh. Ensemble Learning for Appraising English Text Readability using Gompertz Function [J]. Int J Performability Eng, 2023, 19(6): 388-396.
[10]	Nittu Goutham, Karan Singh, Latha Banda, Purushottam Sharma, Chaman Verma, and S. B. Goyal. ShAD-SEF: An Efficient Model for Shilling Attack Detection using Stacking Ensemble Framework in Recommender Systems [J]. Int J Performability Eng, 2023, 19(5): 291-302.
[11]	Pranshu Kumar Soni and Leema Nelson. PCP: Profit-Driven Churn Prediction using Machine Learning Techniques in Banking Sector [J]. Int J Performability Eng, 2023, 19(5): 303-311.
[12]	Mahima Yadav and Ishan Kumar. Image Processing-Based Transliteration from Hindi to English [J]. Int J Performability Eng, 2023, 19(5): 334-341.
[13]	Harshita Batra and Leema Nelson. DCADS: Data-Driven Computer Aided Diagnostic System using Machine Learning Techniques for Polycystic Ovary Syndrome [J]. Int J Performability Eng, 2023, 19(3): 193-202.
[14]	Shobhanam Krishna and Sumati Sidharth. AI-Powered Workforce Analytics: Maximizing Business and Employee Success through Predictive Attrition Modelling [J]. Int J Performability Eng, 2023, 19(3): 203-215.
[15]	Bhagirath, Neetu Mittal, and Sushil Kumar. Impact of Real Time Fraud Prevention on Online Resale Platform using Machine Learning and Device Fingerprint Techniques [J]. Int J Performability Eng, 2023, 19(2): 94-104.