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Research on English Achievement Analysis Based on Improved CARMA Algorithm.

Lin Hu1

  • 1Jilin University of Finance and Economics Jilin, Changchun 130117, China.

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Summary
This summary is machine-generated.

This study enhances the CARMA algorithm for analyzing student English scores, improving mining accuracy by 5.7% and efficiency by 100%. Results highlight homework quality

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Area of Science:

  • Educational Data Mining
  • Computer Science

Background:

  • Student academic performance is influenced by numerous factors.
  • Analyzing these factors requires efficient data mining techniques.

Purpose of the Study:

  • To improve the efficiency and accuracy of the CARMA algorithm for analyzing student English scores.
  • To identify key factors influencing student English performance.

Main Methods:

  • Enhanced the CARMA algorithm by integrating genetic algorithm's crossover and mutation operations.
  • Compared the improved CARMA algorithm with traditional CARMA, FP-Growth, and Apriori algorithms.
  • Applied the improved CARMA algorithm to analyze student English performance data.

Main Results:

  • The improved CARMA algorithm achieved a mining accuracy of 97.985%, an increase from 92.221%.
  • Mining efficiency of the improved CARMA algorithm was double that of other algorithms at 6,500 data points, with efficiency increasing with data volume.
  • Student performance quality strongly correlates with daily homework quality.

Conclusions:

  • The enhanced CARMA algorithm offers superior accuracy and efficiency for educational data mining.
  • Schools should prioritize homework quality to improve student English performance.
  • Further analysis may explore teacher-related factors like gender and professional title.