Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models
Open accessAbstract
Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.
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Gaining insight into how students engage within courses is crucial to improve teaching and learning outcomes and identify when interventions are needed for at-risk students.
“Gaining insight into how students engage within courses is crucial in order to understand how best to improve teaching and learning outcomes and in particular, to identify when interventions are needed for at-risk students (Adnan et al., 2021).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Adnan et al., 2021
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Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138 · see citing works
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- Authors
- Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U.
- Affiliations
- Kohat University of Science and Technology (PK)
- COMSATS University Islamabad (PK)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 7519-7539
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3120327154
- Cited by
- 250
- References
- 63
- Citations by year
- 2026: 46 · 2025: 61 · 2024: 50 · 2023: 48 · 2022: 35 · 2021: 10
- FWCI
- 30.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DMU Open Research Archive (De Montfort University) (submittedVersion)
- Greater South Information System
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- at-risk students, student dropout prediction, early intervention, online learning, learning analytics, clickstream data, student engagement, course duration, random forest, deep learning, virtual learning environments
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