Summary of Early Detection Of At-risk Students Using Machine Learning, by Azucena L. Jimenez Martinez et al.
Early Detection of At-Risk Students Using Machine Learning
by Azucena L. Jimenez Martinez, Kanika Sood, Rakeshkumar Mahto
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research presents preliminary work on identifying at-risk students using machine learning and three unique data categories: engagement, demographics, and performance data from Fall 2023. The goal is to address higher education retention and dropout rates by screening for at-risk students and building a high-risk identification system. By focusing on behavioral factors alongside traditional metrics, this work aims to bridge educational gaps, enhance student outcomes, and boost success across disciplines. Various machine learning models are considered, including Support Vector Machines (SVM), Naive Bayes, K-nearest neighbors (KNN), Decision Trees, Logistic Regression, and Random Forest, which predict at-risk students and identify critical periods of the semester when performance is most vulnerable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to help schools find students who are struggling or might drop out. To do this, they’re using machine learning and three types of data: how engaged students are, demographics like age and gender, and their grades. They want to know which students are at risk of not finishing school so they can help them before it’s too late. The researchers tried different computer models to see which one works best for this task. All the models did a good job predicting which students were at risk, but one called Naive Bayes was the best. |
Keywords
» Artificial intelligence » Dropout » Logistic regression » Machine learning » Naive bayes » Random forest
