Abstract
As the largest distance learning university in the UK, the Open University has more than 250,000 students enrolled, making it also the largest academic institute in the UK. However, many students end up failing or withdrawing from online courses, which makes it extremely crucial to identify those “at risk” students and inject necessary interventions to prevent them from dropping out. This study thus aims at exploring an efficient predictive model, using both behavioural and demographical data extracted from the anonymised Open University Learning Analytics Dataset (OULAD). The predictive model was implemented through machine learning methods that included BART. The analytics indicates that the proposed model could predict the final result of the course at a finer granularity, i.e., classifying the students into Withdrawn, Fail, Pass, and Distinction, rather than only Completers and Non-completers (two categories) as proposed in existing studies. Our model’s prediction accuracy was at 80% or above for predicting which students would withdraw, fail and get a distinction. This information could be used to provide more accurate personalised interventions. Importantly, unlike existing similar studies, our model predicts the final result at the very beginning of a course, i.e., using the first assignment mark, among others, which could help reduce the dropout rate before it was too late.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
The OULAD dataset is released under CC-BY 4.0 licence.
- 3.
References
By The Numbers: MOOCs in 2020 — Class Central. The Report by Class Central, 30 November 2020. https://www.classcentral.com/report/mooc-stats-2020/. Accessed 04 Jan 2021
Study offers data to show MOOCs didn’t achieve their goals | Inside Higher Ed. https://www.insidehighered.com/digital-learning/article/2019/01/16/study-offers-data-show-moocs-didnt-achieve-their-goals. Accessed 04 Jan 2021
Gomez-Zermeno, M.G.,Garza, L.A.D.L.: Research analysis on MOOC course dropout and retention rates (2016). https://doi.org/10.17718/tojde.23429
Dalipi, F., Imran, A.S., Kastrati, Z.: MOOC dropout prediction using machine learning techniques: review and research challenges. In: 2018 IEEE Global Engineering Education Conference (EDUCON), pp. 1007–1014, April 2018. https://doi.org/10.1109/educon.2018.8363340
Borrella, I., Caballero-Caballero, S., Ponce-Cueto, E.: Predict and intervene: addressing the dropout problem in a MOOC-based program. In: Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale, Chicago, IL, USA, June 2019, pp. 1–9. https://doi.org/10.1145/3330430.3333634
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, Doha, Qatar, October 2014, pp. 60–65. https://doi.org/10.3115/v1/w14-4111
Liang, J., Li, C., Zheng, L.: Machine learning application in MOOCs: dropout prediction. In: 2016 11th International Conference on Computer Science Education (ICCSE), August 2016, pp. 52–57. https://doi.org/10.1109/iccse.2016.7581554
Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: MOOC dropout prediction: how to measure accuracy? In: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, Cambridge Massachusetts USA, April 2017, pp. 161–164. https://doi.org/10.1145/3051457.3053974
Cristea, A., Alamri, A., Stewart, C., Alshehri, M., Shi, L.: Earliest predictor of dropout in MOOCs: a longitudinal study of FutureLearn Courses Mizue Kayama, August 2018
Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week’s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) ITS 2019. LNCS, vol. 11528, pp. 163–173. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22244-4_20
Wang, Y., Baker, R.: Content or platform: why do students complete MOOCs? 11(1), 14 (2015)
Uden, L., Sinclair, J., Tao, Y.-H., Liberona, D. (eds.): LTEC 2014. CCIS, vol. 446. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10671-7
Baran, E., Siemens, Baker: Learning analytics and educational data mining: towards communication and collaboration. In: Learning Environments Design Reading Series
Learning analytics | Advance HE. https://www.advance-he.ac.uk/knowledge-hub/learning-analytics. Accessed 29 Mar 2021
Educationaldatamining.org. https://educationaldatamining.org/. Accessed 29 Mar 2021
Liñán, L.C., Pérez, Á.A.J.: Mineria de dades educatives i anàlisi de dades de l’aprenentatge: diferències, semblances i evolució en el temps. RUSC. Univ. Knowl. Soc. J. 12(3) (2015). Article no. 3. https://doi.org/10.7238/rusc.v12i3.2515
Madigan, C.D., Daley, A.J., Kabir, E., Aveyard, P., Brown, W.: Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis. Int. J. Obes. 39(11), 1601–1606 (2015). https://doi.org/10.1038/ijo.2015.116
4 - Prediction.pdf. http://www.cs.stir.ac.uk/courses/ITNP60/lectures/1%20Data%20Mining/4%20-%20Prediction.pdf. Accessed 29 Mar 2021
Klapaftis, Ioannis P., Pandey, S., Manandhar, S.: Graph-based relation mining. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 100–112. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21512-4_12
Guo, P.J., Reinecke, K.: Demographic differences in how students navigate through MOOCs. In: Proceedings of the first ACM conference on Learning @ scale conference, New York, NY, USA, March 2014, pp. 21–30. https://doi.org/10.1145/2556325.2566247
Shi, L., Cristea, A.: Demographic indicators influencing learning activities in MOOCs: learning analytics of FutureLearn Courses, August 2018
Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: Delving deeper into MOOC student dropout prediction. arXiv:1702.06404 [cs], February 2017. http://arxiv.org/abs/1702.06404. Accessed 28 Jan 2021
Brinton, C.G., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), April 2015, pp. 2299–2307. https://doi.org/10.1109/infocom.2015.7218617
Liyanagunawardena, T.R., Williams, S.A.: Dropout: MOOC participants’ perspective’, p. 8
Bolboacă, S.D., Jäntschi, L., Sestraş, A.F., Sestraş, R.E., Pamfil, D.C.: Pearson-Fisher chi-square statistic revisited. Information 2(3) (2011). Article no. 3. https://doi.org/10.3390/info2030528
1.10. Decision Trees — scikit-learn 0.24.1 documentation. https://scikit-learn.org/stable/modules/tree.html. Accessed 29 Mar 2021
Chipman, H.A., George, E.I,. McCulloch, R.E.: BART: Bayesian additive regression trees. arXiv:0806.3286 [stat], October 2010. https://doi.org/10.1214/09-aoas285
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Drousiotis, E., Shi, L., Maskell, S. (2021). Early Predictor for Student Success Based on Behavioural and Demographical Indicators. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-80421-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80420-6
Online ISBN: 978-3-030-80421-3
eBook Packages: Computer ScienceComputer Science (R0)