Analysis of Mooc Data With Educational Data Mining: Systematic Literature Review

Abstract

Participants’ performance is one of the critical factors for the success of the platforms. There is a lot of data in MOOC platforms that are free and open to everyone, and due to this large amount of educational data, it is difficult to make accurate predictions and inferences. The primary purpose of this research is to conduct a literature review to discover the existing Educational Data Mining methods and techniques used to analyze Massive Open Online Course data. For this purpose, the focus is on the source from which the data is collected, which EVM methods and techniques are used, and which tools are used in the analysis to compare different approaches. A total of 32 articles published between 2013-2024 were included in the scope of the study. According to the findings, there are many algorithms used for EVM methods and techniques in the analysis of MOOC data. The most preferred algorithm in the studies is “K-Means”, followed by “Support Vector Machines”, “Decision Trees” and “Random Forest”. Coursera and Edx are among the platforms used and preferred worldwide. It is anticipated that making the data available on these platforms public will contribute to further research and guide studies in the education field. Privacy and ethics also come to the fore within the scope of open data publication. In this context, developing some standards and new approaches to share data with researchers in a standard form that does not include privacy violations will significantly contribute to studies conducted in this field.

Keywords

• Educational Data Mining (EDM)
• Machine Learning Algorithms
• Massive Open Online Courses (MOOCs)

References

1. [1] M. Liu and D. Yu, ‘‘Towards intelligent e-learning systems,’’ Education and Information Technologies, vol. 28, no. 7, pp. 7845–7876, 2023.
2. [2] R. Orman, E. Şimşek, and M. Kozak Çakır, ‘‘Micro-credentials and reflections on higher education,’’ Higher Education Evaluation and Development, vol. 17, no. 2, pp. 96–112, 2023.
3. [3] J. Goopio and C. Cheung, ‘‘The mooc dropout phenomenon and retention strategies,’’ Journal of Teaching in Travel & Tourism, vol. 21, no. 2, pp. 177–197, 2020.
4. [4] P. Diver and I. Martinez, ‘‘Moocs as a massive research laboratory: Opportunities and challenges.’’ Distance Education, vol. 36, no. 1, pp. 5–25, 2015.
5. [5] A. Bozkurt, ‘‘Bağlantıcı kitlesel açik çevrimiçi derslerde etkileşim örüntüleri ve öğreten-öğrenen rollerinin belirlenmesi,’’ Ph.D. dissertation, Anadolu University (Turkey), 2015.
6. [6] T. Jadin and M. Gaisch, ‘‘Extending the moocversity a multi-layered and diversified lens for mooc research.’’ Proceedings of the European MOOC Stakeholder Summit, pp. 73–78., 2014.
7. [7] K. Jordan, ‘‘Massive open online course completion rates revisited: Assessment, length and attrition,’’ The International Review of Research in Open and Distributed Learning, vol. 16, no. 3, pp. 341–358., 2015.
8. [8] B. Prenkaj, P. Velardi, G. Stilo, D. Distante, and S. Faralli, ‘‘A survey of machine learning approaches for student dropout prediction in online courses,’’ ACM Computing Surveys, vol. 53, no. 3, pp. 1–34, 2020.

9. [9] N. Çağıltay, K. Çağıltay, and B. Çelik, ‘‘An analysis of course characteristics, learner characteristics, and certification rates in mitx moocs.’’ The International Review of Research in Open and Distributed Learning, vol. 21, no. 3, pp. 121–139, 2020.
10. [10] D. F. Onah, J. Sinclair, and R. Boyatt, ‘‘Dropout rates of massive open online courses: behavioural patterns.’’ in EDULEARN14 proceedings,, 2014, Conference Proceedings, pp. 5825–5834.
11. [11] C. Romero and S. Ventura, ‘‘Educational data mining: A review of the state of the art,’’ IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, 2010.
12. [12] R. S. Baker and K. Yacef, ‘‘The state of educational data mining in 2009: A review and future visions.’’ JEDM Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009.
13. [13] L. N. M. Bezerra and M. T. Silva, ‘‘Educational data mining applied to a massive course,’’ International Journal Of Distance Education Technologies, vol. 18, no. 4, pp. 17–30, 2020.
14. [14] C. Romero and S. Ventura, ‘‘Educational data mining: A survey from 1995 to 2005,’’ Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, 2007.
15. [15] A. Peña-Ayala, ‘‘Educational data mining: A survey and a data mining-based analysis of recent works. expert systems with applications,’’ Expert systems with applications, vol. 41, no. 4, pp. 1432–1462, 2014.
16. [16] C. Romero and S. Ventura, ‘‘Educational data mining: A survey from 1995 to 2005,’’ Expert Systems with Applications, pp. 135–146, 2017.
17. [17] K. Aulakh, R. K. Roul, and M. Kaushal, ‘‘E-learning enhancement through educational data mining with covid-19 outbreak period in backdrop: A review,’’ International journal of educational development, vol. 101, p. 102814, 2023.
18. [18] J. M. Gallego-Romero, C. Alario-Hoyos, I. Estévez-Ayres, and C. D. Kloos, ‘‘Analyzing learners’ engagement and behavior in moocs on programming with the codeboard ide,’’ Etr&D-Educational Technology Research and Development, vol. 68, no. 5, pp. 2505–2528, 2020.
19. [19] N. Bousbia and I. Belamri, ‘‘Which contribution does edm provide to computer-based learning environments?’’ Educational data mining: Applications and trends, 2014.
20. [20] A. Hicham, A. Jeghal, A. Sabri, and H. Tairi, ‘‘A survey on educational data mining
21. [2014-2019],’’ IEEE, 2020.
22. [21] R. A. Razak, M. Omar, and M. Ahmad, ‘‘A student performance prediction model using data mining technique.’’ International Journal of Engineering & Technology, vol. 7, no. 2.15, pp. 61– 63, 2018.
23. [22] B. Bakhshinategh, O. R. Zaiane, S. ElAtia, and D. Ipperciel, ‘‘Educational data mining applications and tasks: A survey of the last 10 years.’’ Education and Information Technologies, vol. 23, pp. 537–553, 2018.
24. [23] S. Shatnawi, M. M. Gaber, and M. Cocea, ‘‘Text stream mining for massive open online courses: reviewand perspectives,’’ Systems Science&Control Engineering, vol. 2, no. 1, pp. 664–676, 2014.
25. [24] O. R. Yürüm, T. Taskaya-Temizel, and S. Yildirim, ‘‘Predictive video analytics in online courses: A systematic literature review,’’ Technology Knowledge And Learning, 2023.
26. [25] M. E. Buitrago-Ropero, M. S. Ramírez-Montoya, and A. C. Laverde, ‘‘Digital footprints (2005-2019): a systematic mapping of studies in education,’’ Interactive Learning Environments, vol. 31, no. 2, pp. 876–889, 2023.
27. [26] B. Albreiki, N. Zaki, and H. Alashwal, ‘‘Asystematic literature reviewof student’ performance prediction using machine learning techniques,’’ Education Sciences, vol. 11, no. 9, 2021.
28. [27] E. Araka, E. Maina, R. Gitonga, and R. Oboko, ‘‘Research trends in measurement and intervention tools for self-regulated learning for e-learning environmentssystematic review (2008-2018),’’ Research And Practice In Technology Enhanced Learning, vol. 15, no. 1, 2020.
29. [28] M. Bearman, C. D. Smith, A. Carbone, S. Slade, C. Baik, M. Hughes-Warrington, and D. L. Neumann, ‘‘Systematic review methodology in higher education,’’ Higher Education Research & Development, vol. 31, no. 5, pp. 625–640, 2012.
30. [29] B. N. Green, C. D. Johnson, and A. Adams, ‘‘Writing narrative literature reviews for peer-reviewed journals: secrets of the trade,’’ J Chiropr Med, vol. 5, no. 3, pp. 101–17, 2006.
31. [30] M. L. Pan, Preparing literature reviews: Qualitative and quantitative approaches. Taylor & Francis., 2016.
32. [31] P. M. Moreno-Marcos, C. Alario-Hoyos, P. J. Muñoz-Merino, and C. D. Kloos, ‘‘Prediction in moocs: A review and future research directions,’’ IEEE Transactions on Learning Technologies, vol. 12, no. 3, pp. 384–401, 2018.
33. [32] A. Ani and E. T. Khor, ‘‘Development and evaluation of predictive models for predicting students performance in moocs,’’ Education and Information Technologies, 2023.
34. [33] M. Youssef, S. Mohammed, E. Hamada, and B. Wafaa, ‘‘A predictive approach based on efficient feature selection and learning algorithms competition: Case of learners dropout in moocs,’’ Education and Information Technologies, vol. 24, no. 6, pp. 3591–3618, 2019.
35. [34] C. T. Swai and S. E. Mangowi, ‘‘Mining school teachers’ mooc training responses to infer their face-to-face teaching strategy preference,’’ The International Journal of Information and Learning Technology, vol. 39, no. 1, pp. 82–94, 2022.
36. [35] S. Assami, N. Daoudi, and R. Ajhoun, ‘‘Implementation of a machine learning-based mooc recommender system using learner motivation prediction,’’ International Journal of Engineering Pedagogy (iJEP), vol. 12, no. 5, pp. 68–85, 2022.
37. [36] E. Ahmed, ‘‘Student performance prediction using machine learning algorithms.’’ Applied Computational Intelligence and Soft Computing, vol. 1, p. 4067721, 2024.
38. [37] A. Alghamdi, ‘‘Evaluating factors influencing learner satisfaction in massive open online course selection: A data-driven approach using machine learning,’’ Arabian Journal for Science and Engineering, vol. 1, no. 26, 2024.
39. [38] S. Gupta and A. Sabitha, ‘‘Deciphering the attributes of student retention in massive open online courses using data mining techniques,’’ Education and Information Technologies, vol. 24, pp. 1973–1994, 2019.
40. [39] D. J. Lemay and T. Doleck, ‘‘Grade prediction of weekly assignments in moocs: mining video-viewing behavior,’’ Education and Information Technologies, vol. 25, no. 2, pp. 1333–1342, 2019.
41. [40] ——, ‘‘Predicting completion of massive open online course (mooc) assignments from video viewing behavior,’’ Interactive Learning Environments, vol. 30, no. 10, pp. 1782–1793, 2022.
42. [41] D. Liang, J. Y. Jia, X. M.Wu, J. M. Miao, and A. H.Wang, ‘‘Analysis of learners’ behaviors and learning outcomes in a massive open online course,’’ Knowledge Management & E-Learning-An International Journal, vol. 6, no. 3, pp. 281–298, 2014.
43. [42] H. Wan, K. Liu, Q. Yu, and X. Gao, ‘‘Pedagogical intervention practices: Improving learning engagement based on early prediction,’’ IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 278–289, 2019.
44. [43] V. S. Pillutla, A. A. Tawfik, and P. J. Giabbanelli, ‘‘Detecting the depth and progression of learning in massive open online courses by mining discussion data,’’ Technology, Knowledge and Learning, vol. 25, no. 4, pp. 881–898, 2020.
45. [44] S. Li, J. Du, and J. Sun, ‘‘Unfolding the learning behaviour patterns of mooc learners with different levels of achievement,’’ International Journal of Educational Technology in Higher Education, vol. 19, no. 1, 2022.
46. [45] S. Rizvi, B. Rienties, J. Rogaten, and R. F. Kizilcec, ‘‘Investigating variation in learning processes in a futurelearn mooc,’’ Journal of Computing in Higher Education, vol. 32, no. 1, pp. 162–181, 2019.
47. [46] M. Saqr, V. Tuominen, T. Valtonen, E. Sointu, S. Väisänen, and L. Hirsto, ‘‘Teachers learning profiles in learning programming: The big picture!’’ Frontiers in Education, vol. 7, 2022.
48. [47] H. Tang, W. Xing, and B. Pei, ‘‘Exploring the temporal dimension of forum participation in moocs,’’ Distance Education, vol. 39, no. 3, pp. 353–372, 2018.
49. [48] Y. Lee, ‘‘Using self-organizing map and clustering to investigate problem-solving patterns in the massive open online course: An exploratory study,’’ Journal of Educational Computing Research, vol. 57, no. 2, pp. 471–490, 2018.
50. [49] A. Cohen and S. Holstein, ‘‘Analysing successful massive open online courses using the community of inquiry model as perceived by students,’’ Journal Of Computer Assisted Learning, vol. 34, no. 5, pp. 544–556, 2018.
51. [50] Y. Dyulicheva, ‘‘Learning analytics in moocs as an instrument for measuring math anxiety,’’ Voprosy Obrazovaniya / Educational Studies Moscow, no. 4, pp. 243–265, 2021.
52. [51] M. Nilashi, R. A. Abumalloh, M. Zibarzani, S. Samad, W. A. Zogaan, M. Y. Ismail, S. Mohd, and N. A. M. Akib, ‘‘What factors influence students satisfaction in massive open online courses? findings from user-generated content using educational data mining,’’ Education and Information Technologies, vol. 27, no. 7, pp. 9401–9435, 2022.
53. [52] C. Geigle and C. Zhai, ‘‘Modeling mooc student behavior with two-layer hidden markov models,’’ Journal of Educational Data Mining, vol. 9, no. 1, pp. 1–24, 2017.
54. [53] S.-H. Zhong, Y. Li, Y. Liu, and Z. Wang, ‘‘A computational investigation of learning behaviors in moocs,’’ Computer Applications in Engineering Education, vol. 25, no. 5, pp. 693–705, 2017.
55. [54] C. Xu, G. Zhu, J. Ye, and J. Shu, ‘‘Educational data mining: Dropout prediction in xuetangx moocs,’’ Neural Processing Letters, vol. 54, no. 4, pp. 2885–2900, 2022.
56. [55] D. Yang, R. E. Kraut, and C. P. Rose, ‘‘Exploring the effect of student confusion in massive open online courses,’’ Journal of Educational Data Mining, vol. 8, no. 1, pp. 52–83, 2016.
57. [56] Y. Nie, H. Luo, and D. Sun, ‘‘Design and validation of a diagnostic mooc evaluation method combining ahp and text mining algorithms,’’ Interactive Learning Environments, vol. 29, no. 2, pp. 315–328, 2020.
58. [57] S. Batool, J. Rashid, M. W. Nisar, J. Kim, H. Y. Kwon, and A. Hussain, ‘‘Educational data mining to predict students’ academic performance: A survey study,’’ Education and Information Technologies, vol. 28, no. 1, pp. 905–971, 2023.
59. [58] M. M. George and P. S. Rasmi, ‘‘Performance comparison of apache hadoop and apache spark for covid-19 data sets.’’ pp. 1659–1665, 2022, January 2022.
60. [59] D. M. Dener, Murat and A. Orman, ‘‘Açık kaynak kodlu veri madenciliği programları: Weka’da örnek uygulama,’’ Akademik Bilişim, vol. 9, pp. 11–13, 2009.
61. [60] WEKA, 2024. [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/
62. [61] A.-F. J., S. L., G. S., del Jesus M. J., V. S., G. J. M., O. J., R. C., B. J., R. V. M., F. J. C., and H. F.., ‘‘Keel: A software tool to assess evolutionary algorithms to data min-ing problems,’’ Soft Computing, vol. 13, no. 3, pp. 307–318, 2009.
63. [62] ORANGE, 2024. [Online]. Available: http://orange.biolab.si/, (Erişim Tarihi: 2024).
64. [63] R, 2024. [Online]. Available: http://www.r-project.org/