Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

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dc.contributor.authorAdnan, Muhammad
dc.contributor.authorHabib, Asad
dc.contributor.authorAshraf, Jawad
dc.contributor.authorMussadiq, Shafaq
dc.contributor.authorRaza, Arsalan Ali
dc.contributor.authorAbid, Muhammad
dc.contributor.authorBashir, Maryam
dc.contributor.authorKhan, Sana Ullah
dc.date.accessioned2024-10-29T16:39:36Z
dc.date.available2024-10-29T16:39:36Z
dc.date.issued2021-01-05
dc.descriptionopen access article
dc.description.abstractOnline 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%...
dc.funderNo external funder
dc.identifier.citationAdnan, M. et al. (2021) Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models. IEEE Access, (9) pp. 7519 - 7539
dc.identifier.doihttps://doi.org/10.1109/access.2021.3049446
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/2086/24421
dc.publisherIEEE
dc.relation.ispartofIEEE Access
dc.titlePredicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models
dc.typeArticle
oaire.citation.volume9

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