Course Recommendation, Exploratory Data Analysis And Visualizations of Massive Open Online Courses (MOOCS)

  • Noman Islam Karachi Institute of Economics and Technology, Karachi, Pakistan
  • Abdul Rafay Khan NED University of Engineering and Technology Karachi, Pakistan
  • Umair Ahmed NED University of Engineering and Technology Karachi, Pakistan
  • Ahmed Jaffer NED University of Engineering and Technology Karachi, Pakistan
  • Shamim Akhtar College of Applied Medical Sciences, Majmaah University, AL-Majmaah, Saudi Arabia

Abstract

Users and learners nowadays are seeking their education on online platforms such as Open edX, Udemy, Udacity, and Coursera. However, online learners are faced with cumbersome tasks and various challenges to search for the required courses matching their individual goals, knowledge, and interest. With the huge amount of data and material available over the internet for MOOCs, users often face the difficulty of making the right decision to choose the right course that perfectly defines their interest and fulfills their learning requirements. Hence, the lack of targeted recommendations for MOOCs can drive users to choose irrelevant MOOCs. Recommender System (RS) plays a crucial role in assisting learners to find appropriate MOOCs to improve learners’ engagements and their satisfaction/completion rates on the courses that satisfy their learning requirements. Basically, the aim is to visualize the student's areas of interest, which should not differ from the course recommendation and the overall structure of the course.

References

[1] Zheng, Y. and Yang, R.Y., 2017. The rise of MOOCs: The literature review of research progress and hot spots of MOOCs education in mainland China. Eurasia Journal of Mathematics, Science and Technology Education, 13(9), pp.6165-6174.
[2] Bousbahi, F. and Chorfi, H., 2015. MOOC-Rec: a case based recommender system for MOOCs. Procedia-Social and Behavioral Sciences, 195, pp.1813-1822.
[3] Onah, D.F.O. and Sinclair, J.E., 2015. Collaborative filtering recommendation system: a framework in massive open online courses. INTED2015 Proceedings, pp.1249-1257.
[4] Jain, H., 2017. Applying Data Mining Techniques in MOOC Recommender System for Generating Course Recommendations (Doctoral dissertation).
[5] Khalil, H. and Ebner, M., 2014, June. MOOCs completion rates and possible methods to improve retention-A literature review. In EdMedia+ innovate learning (pp. 1305-1313). Association for the Advancement of Computing in Education (AACE).
[6] Rabahallah, K., Mahdaoui, L. and Azouaou, F., 2018. MOOCs Recommender System using Ontology and Memory-based Collaborative Filtering. In ICEIS (1) (pp. 635-641).
[7] Gamage, D., Fernando, S. and Perera, I., 2015, August. Quality of MOOCs: A review of literature on effectiveness and quality aspects. In 2015 8th International Conference on Ubi-Media Computing (UMEDIA) (pp. 224-229). IEEE.
[8] https://www.udemy.com/developers/affiliate/, last accessed on July 2022
[9] Ujjin, S. and Bentley, P.J., 2001. Building a Lifestyle Recommender System. In WWW Posters.
[10] Islam, N., Laeeq, K., Sheikh, J., Ahmed, H. and Sheikh, G.S., 2019. Salaat Ontology: A domain ontology for modeling information related to prayers in Islam. Indian Journal of Science and Technology, 12, p.31.
[11] https://www.coursera.org/, last accessed on July 2022
[12] https://www.edx.com/, last accessed on July 2022
[13] Islam, N., Shafi Sheikh, G., Fatima, R. and Alvi, F., 2019. A Study of Difficulties of Students in Learning Programming. Journal of Education & Social Sciences, 7(2), pp.38-46.
[14] Sheikh, G.S., Islam, N. 2016. A qualitative study of major programming languages: teaching programming languages to computer science students. Journal of Information & Communication Technology (JICT), 10(1), p.11.
[15] Hussain, F., Saeed, U., Muhammad, G., Islam, N. and Sheikh, G.S., 2019. Classifying cancer patients based on DNA sequences using machine learning. Journal of Medical Imaging and Health Informatics, 9(3), pp.436-443.
[16] Yuan, X., Han, L., Qian, S., Xu, G. and Yan, H., 2019. Singular value decomposition based recommendation using imputed data. Knowledge-Based Systems, 163, pp.485-494.
[17] Feng, W., Tang, J. and Liu, T.X., 2019, July. Understanding dropouts in MOOCs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 517-524).
[18] Bisong, E., 2019. Matplotlib and Seaborn. In Building machine learning and deep learning models on google cloud platform (pp. 151-165). Apress, Berkeley, CA.
[19] Islam, N., 2019, December. A novel context-aware caching scheme for 5G networks. In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-7). IEEE.
[20] Zhang, F., Gong, T., Lee, V.E., Zhao, G., Rong, C. and Qu, G., 2016. Fast algorithms to evaluate collaborative filtering recommender systems. Knowledge-Based Systems, 96, pp.96-103.
Published
2022-07-28