Early Detection of Under-Performing Students Using Machine Learning Algorithms
Author:
Affiliation:
1. The University of Newcastle,School of Information and Physical Sciences,Callaghan,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9719589/9719877/09719896.pdf?arnumber=9719896
Reference18 articles.
1. Mining Students' Data for Prediction Performance
2. DSS-PSP - A Decision Support Software for Evaluating Students’ Performance
3. Models for early prediction of at-risk students in a course using standards-based grading
4. Tracking Student Performance in Introductory Programming by Means of Machine Learning
5. Building Course-Specific Regression-based Models to Identify At-risk Students
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1. Evaluating the student performance prediction and action framework through a learning analytics intervention study;Education and Information Technologies;2024-08-06
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