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A machine learning based method for constructing group profiles of university students.

Ran Song1,2, Fei Pang3, Hongyun Jiang1

  • 1School of Mathematics, Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China.

Heliyon
|April 11, 2024
PubMed
Summary
This summary is machine-generated.

This study develops a novel student profiling model using K-means clustering and a Back Propagation neural network, achieving 90.22% accuracy in classifying four distinct student profiles for enhanced educational development.

Keywords:
Classification predictionGroup profilingK-means clusteringNeural networkQuestionnaire survey

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Area of Science:

  • Educational Technology
  • Data Science in Education
  • Higher Education Research

Background:

  • Digital transformation in higher education necessitates advanced student profiling.
  • Existing student profiling methods often lack comprehensiveness and rely on single data sources.
  • Big data technology enables sophisticated analysis of student educational development.

Purpose of the Study:

  • To construct a predictive student profiling model using questionnaire data.
  • To enhance the effectiveness of educational questionnaires through improved student classification.
  • To address limitations of previous studies by employing a comprehensive, multi-attribute approach.

Main Methods:

  • Utilized questionnaire surveys to collect diverse student data.
  • Applied K-means clustering algorithm for initial student data grouping.
  • Developed a categorical prediction model using Back Propagation neural networks for classification.

Main Results:

  • Identified four distinct student profiles: Diligent Learners, Earnest Individuals, Discerning Achievers, and Moral Advocates.
  • Successfully labeled student groups based on identified profiles.
  • Achieved a high classification accuracy of 90.22% for the prediction model.

Conclusions:

  • The developed model offers a novel and effective method for university student profiling.
  • The findings provide a valuable methodological reference for educational researchers and institutions.
  • The study enhances the utility of questionnaire-based approaches in higher education.