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. 2024 Dec 13:7:1449572.
doi: 10.3389/fdata.2024.1449572. eCollection 2024.

Predicting student self-efficacy in Muslim societies using machine learning algorithms

Mohammed Ba-Aoum  1   2 , Mohammed Alrezq  1 , Jyotishka Datta  3 , Konstantinos P Triantis  1
Affiliations

Predicting student self-efficacy in Muslim societies using machine learning algorithms

Mohammed Ba-Aoum et al. Front Big Data. .

Abstract

Introduction: Self-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.

Methods: An empirical dataset collected was used to examine self-efficacy among secondary school students in Muslim societies. Four machine learning algorithms-Decision Tree, Random Forest, XGBoost, and Neural Network-were employed to predict self-efficacy using two demographic variables and 10 socio-emotional, cognitive, and regulatory factors. The predictors included culturally relevant variables such as religious/spiritual beliefs and collectivist-individualist orientation. Model performance was assessed using root mean square error (RMSE) and r-squared (R 2) metrics to ensure reliability and validity.

Results: The results showed that Random Forest outperformed the other models in accuracy, as measured by R 2 and RMSE metrics. Among the predictors, self-regulation, problem-solving, and a sense of belonging emerged as the most significant factors, contributing to more than half of the model's predictive power. Other variables such as gratitude, forgiveness, empathy, and meaning-making displayed moderate predictive value, while gender, emotion regulation, and collectivist-individualist orientation had minimal impact. Notably, religious/spiritual beliefs and regional factors showed negligible influence on self-efficacy predictions.

Discussion: This study enhances the understanding of factors influencing self-efficacy among students in Muslim societies and offers a data-driven foundation for developing targeted educational interventions. The findings highlight the utility of machine learning in education research, demonstrating its ability to uncover insights for equitable and effective decision-making. By emphasizing the importance of regulatory and socio-emotional factors, this research provides actionable insights to elevate student performance and well-being in diverse cultural contexts.

Keywords: Muslim societies; academic performance; educational equity; machine learning; self-efficacy; self-regulation; socio-emotional learning; student wellbeing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Machine learning framework used in this study.
Figure 2
Figure 2
Correlations between variables.
Figure 3
Figure 3
Distribution among variables based on gender.
Figure 4
Figure 4
Frequency per variable.
Figure 5
Figure 5
Importance of variables based on random forest.
Figure 6
Figure 6
Importance of variables based on the four machine learning algorithms.
See this image and copyright information in PMC

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