Predicting Student Dropout in a MOOC: An Evaluation of a Deep Neural Network Model
Abstract
Massive Open Online Courses (MOOCs) have transformed the way educational institutions deliver high-quality educational material to the onsite and distance learners across the globe. As a result, a new paradigm shifts as to how learners acquire and benefit from the wealth of knowledge provided by a MOOC at their doorstep nowadays in contrast to the brick and mortar settings is visible. Learners are therefore showing a profound interest in the MOOCs offered by top universities and industry giants. They have also attracted a vast number of students from far-flung areas of the world. The massive number of registered students in MOOCs, however, pose one major challenge, i.e., 'the dropouts'. Course planners and content providers are struggling to retain the registered students, which give rise to a new research agenda focusing on predicting and explaining student dropout and low completion rates in a MOOC. Machine learning techniques utilizing deep learning approaches can efficiently predict the potential dropouts and can raise an alert well before time. In this paper, we have focused our study on the application of feed-forward deep neural network architectures to address this problem. Our model achieves not only high accuracy, but also low false negative rate while predicting dropouts on the MOOC data. Moreover, we also provide an in-depth comparison of the proposed architectures concerning precision, recall, and F1 measure.
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Recent developments in predicting student attrition propose the use of data acquired from disparate sources in addition to more sophisticated algorithms such as deep feed-forward neural networks and hybrid logit leaf model.
“Recent developments in predicting student attrition propose the use of data acquired from disparate sources in addition to more sophisticated algorithms such as deep feed-forward neural networks (Imran et al., 2019) and hybrid logit leaf model (Coussement et al., 2020).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Imran et al., 2019
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- Authors
- Imran, A. S., Dalipi, F., & Kastrati, Z.
- Affiliations
- Norwegian University of Science and Technology (NO)
- University of South-Eastern Norway (NO)
- Linnaeus University (SE)
- Venue
- Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence
- Publisher
- ACM
- Published
- 2019-04-19
- Pages
- 190-195
- OpenAlex
- W2966808979
- Cited by
- 36
- References
- 18
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 6 · 2021: 13 · 2020: 4
- FWCI
- 6.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computing and Artificial Intelligence (ICCAI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Demography › Evasion and Academic Success Factors
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- student dropout prediction, MOOCs, massive open online courses, course completion, student retention, feedforward neural networks, deep learning, online learning
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