Early Detection of At-Risk Students in a Calculus Course

Dileep, Akshay Kumar; Bansal, Ajay; Cunningham, James

doi:10.1109/compsac54236.2022.00034

Cited by 3 publications

(1 citation statement)

References 6 publications

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“… Increase model accuracy by modifying model: Some researchers are attempting to increase model accuracy by modifying algorithms (Cabezuelo et al, 2023) and performing additional hyperparameter tuning (Dileep et al, 2022). They are also looking into ensemble classification (Pratama et al, 2021) or using a voting scheme of machine learning algorithms to increase the model accuracy (Palacios et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Advancements in Machine Learning Techniques for Educational Data Mining: An Overview of Perspectives and Trends

Kannan,

Yap,

et al. 2023

ijmst

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In the educational data mining (EDM) field, predicting student at-risk, student retention, dropout and performance have been attractive tasks among researchers. However, it is difficult to develop accurate models without first performing proper feature selection and class balancing. Therefore, the goal of this study is to review the current and future perspective and trends within the field of EDM for the past 10 years. The goal is to understand the state-of-the-art methods and techniques involving feature selection, class balancing and machine learning models. From the analysis, it is understood that there are plenty of research gaps yet to be explored.

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