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Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine

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  • 1Department of Computer Science, Iqra National University, Peshawar, Pakistan.

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|April 16, 2024
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Summary
This summary is machine-generated.

Machine learning effectively predicts student academic performance, forecasting grades and marks using historical data. This approach aids in enhancing educational standards and planning for future development in educational data mining.

Keywords:
Educational data-mining (EDM)Learning analyticsMachine-learning (ML)Students’ performance predictionSupervised learning

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Area of Science:

  • Educational Data Mining
  • Machine Learning Applications

Background:

  • Student academic performance prediction is a complex challenge in educational data mining.
  • Academic success depends on multiple influencing factors, necessitating advanced analytical methods.

Purpose of the Study:

  • To forecast student grades and marks using supervised machine learning (ML) techniques.
  • To analyze the quality of education and its relation to sustainability goals through data analysis.

Main Methods:

  • Utilized a dataset from the Board of Intermediate & Secondary Education (BISE) Peshawar.
  • Pre-processed historical student data with 30 attributes.
  • Trained a regression model for marks prediction and a decision tree (DT) classifier for grade prediction.

Main Results:

  • Machine learning models demonstrated efficiency and relevance in predicting student academic performance.
  • The study validated the utility of ML in analyzing educational data for actionable insights.

Conclusions:

  • ML technology is a valuable tool for forecasting student performance, offering insights for educational improvement.
  • Data analysis from educational systems can inform planning and enhance the quality of education nationwide.