A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs (original) (raw)

Abstract

The increasing popularity of massively online open courses (MOOCs) has been attracting a lot of learners. Despite the popularity, it has been observed that there is a significant percentage of learners who discontinue courses and drop out of the platform. This is a problem that most of the MOOC courses face. The dropout probability of any student depends on his/her interaction with the platform, and the features of the course in which the student has enrolled. The research work is intended to study and analyze the dropout behavior of the students in online learning with identification of the reasons and to understand their impact. The current research accounts for the activity log of learners of 13 different online courses offered by Harvard and MIT during 2012 to 2013. The work examines the attributes which affects the student dropout rate. The research can be useful in improving the existing features of the MOOC courses and content to ensure persistence turnout of their learners.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

The alternative text for this image may have been generated using AI.

Fig. 2

The alternative text for this image may have been generated using AI.

Fig. 3

The alternative text for this image may have been generated using AI.

Fig. 4

The alternative text for this image may have been generated using AI.

Fig. 5

The alternative text for this image may have been generated using AI.

Fig. 6

The alternative text for this image may have been generated using AI.

Fig. 7

The alternative text for this image may have been generated using AI.

Fig. 8

The alternative text for this image may have been generated using AI.

Fig. 9

The alternative text for this image may have been generated using AI.

Similar content being viewed by others

References

  1. Allione G, Stein RM (2016) Mass attrition: an analysis of drop out from principles of microeconomics MOOC. J Econ Educ 47(2):174–186
    Article Google Scholar
  2. Balakrishnan G, Coetzee D (2013) Predicting student retention in massive open online courses using hidden Markov models. Electr Eng Comput Sci Univ Calif Berkeley 53:57–58
    Google Scholar
  3. Banerjee AV, Duflo E (2014) (Dis) organization and success in an economics MOOC. Am Econ Rev 104(5):514–518
    Article Google Scholar
  4. Boyer S, Veeramachaneni K (2015) Transfer learning for predictive models in massive open online courses. In: International conference on artificial intelligence in education. Springer, Cham, pp 54–63
  5. Cohen A, Baruth O (2017) Personality, learning, and satisfaction in fully online academic courses. Comput Hum Behav 72:1–12
    Article Google Scholar
  6. Coleman CA, Seaton DT, Chuang I (2015) Probabilistic use cases: discovering behavioral patterns for predicting certification. In: Proceedings of the second (2015) ACM conference on learning@ scale, pp 141–148
  7. Gomez-Zermeno MG, Aleman De la Garza L (2016) Research analysis on MOOC course dropout and retention rates. Turk Online J Distance Educ 17(2):3–14
    Google Scholar
  8. Gupta S, Sabitha AS (2019) Deciphering the attributes of student retention in massive open online courses using data mining techniques. Educ Inf Technol 24(3):1973–1994
    Article Google Scholar
  9. He J, Bailey J, Rubinstein BI, Zhang R (2015) Identifying at-risk students in massive open online courses. In: Twenty-ninth AAAI conference on artificial intelligence
  10. Ho A, Reich J, Nesterko S, Seaton D, Mullaney T, Waldo J, Chuang I (2014) HarvardX and MITx: the first year of open online courses, fall 2012-summer 2013. HarvardX and MITx working paper no. 1
  11. Jacobsen DY (2019) Dropping out or dropping in? A connectivist approach to understanding participants’ strategies in an e-learning MOOC pilot. Technol Knowl Learn 24(1):1–21
    Article Google Scholar
  12. Kizilcec RF, Halawa S (2015) Attrition and achievement gaps in online learning. In: Proceedings of the second (2015) ACM conference on learning@ scale, pp 57–66
  13. Liyanagunawardena TR, Lundqvist KØ, Williams SA (2015) Who are with us: MOOC learners on a FutureLearn course. Br J Educ Technol 46(3):557–569. https://doi.org/10.1111/bjet.12261
    Article Google Scholar
  14. Onah DF, Sinclair J, Boyatt R, Foss J (2014) Massive open online courses: learner participation. In: Proceeding of the 7th international conference of education, research and innovation, pp 2348–2356
  15. Ramesh A, Goldwasser D, Huang B, Daumé H III, Getoor L (2013) Modeling learner engagement in MOOCs using probabilistic soft logic. In: NIPS workshop on data driven education, vol 21, p 62
  16. Rosé CP, Carlson R, Yang D, Wen M, Resnick L, Goldman P, Sherer J (2014) Social factors that contribute to attrition in MOOCs. In: Proceedings of the first ACM conference on learning@ scale conference, pp 197–198
  17. Taylor C, Veeramachaneni K, O'Reilly UM (2014) Likely to stop? predicting stopout in massive open online courses. arXiv preprint arXiv:1408.3382
  18. Wen M, Yang D, Rose C (2014) Sentiment analysis in MOOC discussion forums: what does it tell us? In: Educational data mining 2014
  19. Yang D, Sinha T, Adamson D, Rosé CP (2013) Turn on, tune in, drop out: anticipating student dropouts in massive open online courses. In: Proceedings of the 2013 NIPS DATA-driven education workshop, vol 11, p 14
  20. Cui Y, Jin WQ, Wise AF (2017) Humans and machines together: improving characterization of large scale online discussions through dynamic interrelated post and thread categorization (DIPTiC). Paper presented at the L@S 2017—Proceedings of the 4th (2017) ACM conference on learning at scale, pp 217–219. https://doi.org/10.1145/3051457.3053989. www.scopus.com
  21. Cui Y, Wise AF (2015) Identifying content-related threads in MOOC discussion forums. Paper presented at the L@S 2015—2nd ACM conference on learning at scale, pp 299–303. https://doi.org/10.1145/2724660.2728679. www.scopus.com
  22. Cui Y, Wise AF, Allen KL (2019) Developing reflection analytics for health professions education: a multi-dimensional framework to align critical concepts with data features. Comput Hum Behav. https://doi.org/10.1016/j.chb.2019.02.019
    Article Google Scholar
  23. Wise AF (2018) Learning analytics: using data-informed decision-making to improve teaching and learning. In: Contemporary technologies in education: maximizing student engagement, motivation, and learning, pp 119–143. https://doi.org/10.1007/978-3-319-89680-9_7. www.scopus.com
  24. Wise AF, Cui Y (2018) Envisioning a learning analytics for the learning sciences. In: Proceedings of international conference of the learning sciences, ICLS, vol 3, pp 1799–1806. www.scopus.com
  25. Wise AF, Cui Y (2018) Learning communities in the crowd: characteristics of content related interactions and social relationships in MOOC discussion forums. Comput Educ 122:221–242. https://doi.org/10.1016/j.compedu.2018.03.021
    Article Google Scholar
  26. Wise AF, Cui Y (2018) Unpacking the relationship between discussion forum participation and learning in MOOCs: content is key. Paper presented at the ACM international conference proceeding series, pp 330–339. https://doi.org/10.1145/3170358.3170403. www.scopus.com
  27. Wise AF, Cui Y, Jin WQ (2017) Honing in on social learning networks in MOOC forums: examining critical network definition decisions. Paper presented at the ACM international conference proceeding series, pp 383–392. https://doi.org/10.1145/3027385.3027446www.scopus.com
  28. Wise AF, Cui Y, Vytasek J (2016) Bringing order to chaos in MOOC discussion forums with content-related thread identification. Paper presented at the ACM international conference proceeding series, 25–29-April-2016, pp 188–197. https://doi.org/10.1145/2883851.2883916. www.scopus.com
  29. Wise AF, Schwarz BB (2017) Visions of CSCL: eight provocations for the future of the field. Int J Comput Support Collab Learn 12(4):423–467. https://doi.org/10.1007/s11412-017-9267-5
    Article Google Scholar
  30. http://edutechwiki.unige.ch/en/RapidMiner_Studio
  31. https://www.classcentral.com/report/edx-2018-review/
  32. https://www.ef.com/wwen/epi/
  33. https://www.kaggle.com/kanikanarang94/mooc-dataset

Download references

Acknowledgements

The authors are thankful to anonymous reviewers for their fruitful suggestions. Ms. Chhaya Khattri is thankful to Dr. A Sai Sabitha for her initial support and encouragement towards the start of the work.

Author information

Authors and Affiliations

  1. Department of Information Technology (DIT), Amity School of Engineering and Technology (ASET), Amity University Uttar Pradesh (AUUP), Sector-125, Noida, Gautam Buddha Nagar, Uttar Pradesh, 201313, India
    Seema Rawat & Chhaya Khattri
  2. Amity Institute of Geoinformatics and Remote Sensing (AIGIRS), Amity University Uttar Pradesh (AUUP), Sector-125, Noida, Gautam Buddha Nagar, Uttar Pradesh, 201313, India
    Deepak Kumar
  3. Department of Computer Science and Engineering (DCSE), Amity School of Engineering and Technology (ASET), Amity University Uttar Pradesh (AUUP), Sector-125, Noida, Gautam Buddha Nagar, Uttar Pradesh, 201313, India
    Praveen Kumar

Authors

  1. Seema Rawat
  2. Deepak Kumar
  3. Praveen Kumar
  4. Chhaya Khattri

Contributions

Dr. Seema Rawat conceived and designed the study, Ms. Chhaya Khattri performed the research, Dr. Deepak Kumar analyzed the data, and Dr. Praveen Kumar contributed to editorial input.

Corresponding author

Correspondence toDeepak Kumar.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Cite this article

Rawat, S., Kumar, D., Kumar, P. et al. A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs.Neural Comput & Applic 33, 14823–14835 (2021). https://doi.org/10.1007/s00521-021-06122-3

Download citation

Keywords

Profiles

  1. Seema Rawat View author profile
  2. Deepak Kumar View author profile
  3. Praveen Kumar View author profile