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Pei, Bo; Xing, Wanli – Journal of Educational Computing Research, 2022
This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…
Descriptors: At Risk Students, Learning Analytics, Information Retrieval, Models
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Barragán, Sandra; González, Leandro; Calderón, Gloria – Interchange: A Quarterly Review of Education, 2022
A combination of mathematical and statistical modelling techniques may be used to analyse student dropout behaviour. The aim of this study is to combine Survival Analysis and Analytic Hierarchy Process methodologies when identifying students at-risk of dropping out. This combination favours the institutional understanding of dropout as a dynamic…
Descriptors: Undergraduate Students, Gender Differences, Age Differences, Decision Making
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Kay, Ellie; Bostock, Paul – Student Success, 2023
Providing timely nudges to students has been shown to improve engagement and persistence in tertiary education. However, many studies focus on small-scale pilots rather than institution-wide initiatives. This article assesses the impact of a pan-institution Early Alert System at the University of Canterbury that utilises nudging when students are…
Descriptors: At Risk Students, Learner Engagement, Undergraduate Students, Handheld Devices
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Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
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Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses
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Botelho, Anthony F.; Varatharaj, Ashvini; Patikorn, Thanaporn; Doherty, Diana; Adjei, Seth A.; Beck, Joseph E. – IEEE Transactions on Learning Technologies, 2019
The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are…
Descriptors: Student Attrition, Student Behavior, Early Intervention, Identification