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Related Experiment Video

Updated: May 7, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Predicting student self-efficacy in Muslim societies using machine learning algorithms.

Mohammed Ba-Aoum1,2, Mohammed Alrezq1, Jyotishka Datta3

  • 1Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States.

Frontiers in Big Data
|December 30, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models identified self-regulation, problem-solving, and belonging as key predictors of student self-efficacy in Muslim societies. These factors significantly influence academic success and well-being, guiding targeted interventions.

Keywords:
Muslim societiesacademic performanceeducational equitymachine learningself-efficacyself-regulationsocio-emotional learningstudent wellbeing

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Area of Science:

  • Educational Psychology
  • Machine Learning in Education
  • Sociocultural Influences on Learning

Background:

  • Self-efficacy is crucial for academic success and life outcomes.
  • Research on self-efficacy predictors using machine learning is limited, especially in Muslim societies.
  • This study addresses this gap by analyzing factors influencing student self-efficacy in these contexts.

Purpose of the Study:

  • To identify key predictors of self-efficacy among secondary school students in Muslim societies.
  • To leverage machine learning models for analyzing these predictors.
  • To provide a data-driven foundation for educational interventions.

Main Methods:

  • Empirical dataset of secondary school students in Muslim societies.
  • Four machine learning algorithms (Decision Tree, Random Forest, XGBoost, Neural Network) were used.
  • Predictors included demographic, socio-emotional, cognitive, and regulatory factors, with culturally relevant variables.

Main Results:

  • Random Forest model demonstrated superior accuracy (R-squared and RMSE).
  • Self-regulation, problem-solving, and sense of belonging were the most significant predictors.
  • Gratitude, forgiveness, empathy, and meaning-making showed moderate influence; gender, emotion regulation, and collectivist-individualist orientation had minimal impact.

Conclusions:

  • Machine learning effectively identifies self-efficacy predictors in diverse cultural contexts.
  • Self-regulation and socio-emotional factors are critical for student performance and well-being.
  • Findings support targeted educational interventions to enhance student outcomes in Muslim societies.