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Machine Learning Grade Prediction Using Students' Grades and Demographics

arXiv – CS AI|Mwayi Sonkhanani, Symon Chibaya, Clement N. Nyirenda||1 views
🤖AI Summary

Researchers developed a unified machine learning framework that predicts both pass/fail outcomes and continuous grades for secondary school students with up to 96% accuracy. The study of 4424 students demonstrates how AI can enable early identification of at-risk students and optimize educational resource allocation through data-driven predictions.

Key Takeaways
  • Machine learning models achieved 96% accuracy in predicting student pass/fail outcomes using academic and demographic data.
  • The unified framework simultaneously handles classification and regression tasks, improving upon traditional separate-task approaches.
  • Study analyzed 4424 secondary school students to develop early warning systems for academic failure.
  • Results show coefficient of determination of 0.70 for grade prediction, enabling personalized educational interventions.
  • Framework offers practical solution for reducing grade repetition and optimizing resource allocation in schools.
Read Original →via arXiv – CS AI
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