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Updated: Sep 19, 2025

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Research on learning achievement classification based on machine learning.

Jianwei Dong1,2, Ruishuang Sun3, Zhipeng Yan4

  • 1College of Educational Science, Xinjiang Normal University, Urumqi, China.

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|June 18, 2025
PubMed
Summary
This summary is machine-generated.

Predicting student academic achievement is crucial for education. This study enhances classification accuracy using Gaussian Distribution based Data Augmentation (GDO) and machine learning models, achieving a 94.12% accuracy rate.

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Area of Science:

  • Educational Data Mining
  • Machine Learning in Education
  • Artificial Intelligence in Education

Background:

  • Academic achievement is a key indicator of educational quality and student learning outcomes.
  • Traditional academic performance classification methods suffer from low accuracy and struggle with nonlinear relationships and data sparsity.
  • Accurate prediction of academic achievement can inform educational strategies and policy development.

Purpose of the Study:

  • To analyze diverse student characteristics influencing academic performance.
  • To improve the accuracy and robustness of student performance classification using advanced computational techniques.
  • To explore the efficacy of various machine learning and deep learning models combined with data augmentation for grade classification.

Main Methods:

  • Analysis of student data including personal information, academic records, attendance, family background, and extracurricular activities.
  • Application of Gaussian Distribution based Data Augmentation (GDO) to enhance data quality and model robustness.
  • Evaluation of multiple Machine Learning (ML) and Deep Learning (DL) models, including Radial Basis Function Network (RBFN), for classification tasks with varied feature combinations and augmentation strategies.

Main Results:

  • The RBFN model, utilizing educational habit features and GDO data augmentation, achieved the highest performance.
  • Achieved a classification accuracy of 94.12% and an F1 score of 94.46% with the optimal model and feature set.
  • Validated the effectiveness of synthetic data through variance homogeneity and P-value analysis, and assessed the impact of oversampling rates.

Conclusions:

  • The proposed GDO technique combined with ML/DL models significantly improves student grade classification accuracy and robustness.
  • Educational habit features are highly predictive of academic performance when augmented with GDO.
  • This research offers valuable insights for educational data analysis, student intervention strategies, and the advancement of intelligent education systems.