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Gender prediction based on University students' complex thinking competency: An analysis from machine learning

Gerardo Ibarra-Vazquez1, María Soledad Ramí Rez-Montoya1, Hugo Terashima2

  • 1Institute for the Future of Education, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey, 64849 Nuevo León Mexico.

Education and Information Technologies
|June 26, 2023
PubMed
Summary

Machine learning models accurately classified student gender based on complex thinking perception, achieving high accuracy in training and testing. However, models showed bias, frequently misclassifying males as females, highlighting the need for further research in educational technology.

Keywords:
Complex thinkingEducational innovationGender predictionHigher educationMachine learningReasoning for complexity

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Area of Science:

  • Educational Psychology
  • Computer Science
  • Data Science

Background:

  • Student gender classification is crucial for personalized education.
  • Perception of complex thinking competency can vary between genders.
  • Machine learning offers novel approaches to analyze educational data.

Purpose of the Study:

  • To evaluate machine learning models for classifying student gender based on complex thinking perception.
  • To analyze model performance and prediction bias.
  • To explore applications in adaptive educational strategies.

Main Methods:

  • Utilized data from 605 university students in Mexico using the eComplexity instrument.
  • Applied four machine learning models: Random Forest, Support Vector Machines, Multi-layer Perception, and 1D Convolutional Neural Network.
  • Conducted training/testing analysis and confusion matrix analysis, including oversampling for dataset imbalance.

Main Results:

  • Models achieved high classification accuracy: 96.94% (training) and 82.14% (testing).
  • Confusion matrix analysis revealed prediction bias across all models.
  • The most common error was misclassifying male students as female.

Conclusions:

  • Machine learning models can effectively differentiate gender based on complex thinking perception data.
  • Prediction bias necessitates careful consideration and further mitigation strategies.
  • This research supports using ML in survey analysis for novel educational practices to reduce gender-based social gaps.