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Machine learning-driven development of a behaviour-based student classification system (SCS-B) for enhanced

E S Vinoth Kumar1, R Augustian Isaac2, P Sundaravadivel3

  • 1Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India.

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|October 28, 2025
PubMed
Summary

This study introduces a behavior-based student classification system (SCS-B) using machine learning to predict student performance. The model effectively identifies academic and behavioral patterns, enhancing educational data analysis and student support.

Keywords:
BehaviourClassification accuracyGenetic algorithmMachine learningOutlier detectionPipelineStudent performance

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Area of Science:

  • Educational Data Mining
  • Machine Learning
  • Learning Analytics

Background:

  • Evaluating student performance is crucial for improving educational standards and addressing achievement gaps.
  • Educational institutions worldwide invest in understanding student performance to enhance outcomes.
  • Predictive models are essential for early intervention to improve overall student results.

Purpose of the Study:

  • To develop a behavior-based student classification system (SCS-B) using machine learning.
  • To collect and analyze student academic and behavioral data for performance prediction.
  • To enhance the accuracy and efficiency of student performance evaluation.

Main Methods:

  • Data collection via questionnaires focusing on academic and behavioral features.
  • Data pre-processing including singular value decomposition, outlier detection, and dimensionality reduction.
  • Model training using a genetic algorithm to optimize performance and avoid local minima.

Main Results:

  • The SCS-B model demonstrates superior classification accuracy.
  • The system requires minimal processing time for extensive student datasets.
  • Effective identification of student performance patterns based on behavior and academics.

Conclusions:

  • The developed behavior-based student classification system (SCS-B) is effective for educational data analysis.
  • Machine learning, particularly with genetic algorithm optimization, offers a robust approach to student performance prediction.
  • The SCS-B system provides a valuable tool for educational institutions to identify and support students.