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Predicting Academic Performance of Students Using a Hybrid Data Mining Approach.

Bindhia K Francis1,2, Suvanam Sasidhar Babu3

  • 1Bharathiar University, Coimbatore, India. dhiyajoji23@gmail.com.

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|May 1, 2019
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Summary

This study introduces a hybrid algorithm combining clustering and classification for predicting student academic performance. The novel approach significantly improves prediction accuracy in higher education institutions.

Keywords:
Educational data miningK-means clusteringPrediction accuracyStudent academic performance

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Area of Science:

  • Educational Data Mining
  • Machine Learning in Education

Background:

  • Educational institutions generate vast amounts of student data.
  • Analyzing this data can reveal patterns in student learning behavior.
  • Educational data mining (EDM) techniques are crucial for understanding student performance.

Purpose of the Study:

  • To develop a novel prediction algorithm for evaluating academic performance.
  • To enhance the accuracy of student performance prediction in higher education.

Main Methods:

  • A hybrid algorithm integrating classification and clustering techniques was developed.
  • The algorithm was tested using a real-time student dataset from various academic disciplines.
  • The study focused on higher educational institutions in Kerala, India.

Main Results:

  • The hybrid algorithm demonstrated superior accuracy in predicting student academic performance.
  • Combining clustering and classification approaches yielded significant improvements over traditional methods.
  • The developed algorithm effectively identifies patterns in student data for performance evaluation.

Conclusions:

  • The hybrid clustering and classification algorithm offers a more accurate method for predicting student academic success.
  • EDM is a valuable tool for educational institutions to identify and support student performance.
  • This research provides a robust framework for academic performance prediction in higher education.