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Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis.

Yupei Zhang1,2, Yue Yun1,2, Rui An1,2

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, China.

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|December 24, 2021
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Summary
This summary is machine-generated.

This study reviews student performance prediction (SPP) methods for personalized education. It outlines SPP stages and discusses challenges and future directions in educational data mining.

Keywords:
educational data mining (EDM)pattern recognitionpersonalized educationreview and discussionstudent performance prediction

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Area of Science:

  • Artificial Intelligence
  • Educational Data Mining (EDM)

Background:

  • Student Performance Prediction (SPP) is crucial for personalized education.
  • SPP has gained significant attention in AI and EDM.

Purpose of the Study:

  • To systematically review Student Performance Prediction (SPP) studies.
  • To analyze SPP from machine learning and data mining perspectives.
  • To identify current challenges and future research directions in SPP.

Main Methods:

  • A systematic review of SPP literature.
  • Partitioning SPP into five key stages: data collection, problem formalization, model development, prediction, and application.
  • Experimental validation using institutional and public datasets.

Main Results:

  • The review categorizes SPP into five distinct stages.
  • Experiments were conducted on datasets from China and public sources.
  • Identified shortcomings and future research avenues were discussed.

Conclusions:

  • SPP is a vital component of personalized education.
  • The review provides a structured overview of SPP methodologies.
  • This work facilitates advancements in SPP and personalized learning.