Predicting Student Self-Efficacy in Muslim Societies Using Machine Learning
Author Information
Author(s): Ba-Aoum Mohammed, Alrezq Mohammed, Datta Jyotishka, Triantis Konstantinos P.
Primary Institution: Virginia Tech
Hypothesis
What are the factors that are most important in predicting the self-efficacy of secondary school students in Muslim societies?
Conclusion
The study found that self-regulation, problem-solving, and a sense of belonging are the most significant predictors of student self-efficacy in Muslim societies.
Supporting Evidence
- Self-regulation was identified as the most important predictor of self-efficacy.
- Problem-solving skills were also found to significantly influence self-efficacy.
- A sense of belonging in the school environment positively affected students' self-efficacy.
- Gratitude, forgiveness, empathy, and meaning-making showed moderate importance in predicting self-efficacy.
Takeaway
This study looked at what helps students in Muslim societies believe in themselves and do well in school, finding that being able to manage oneself, solve problems, and feel like they belong are really important.
Methodology
The study used machine learning algorithms including Decision Tree, Random Forest, XGBoost, and Neural Network to analyze data from a survey of secondary school students.
Limitations
The study focused only on secondary school students and did not consider other potential factors like family support and socioeconomic status.
Participant Demographics
Participants were secondary school students under 18 years old from Muslim-majority societies, with a majority being female (61.13%).
Digital Object Identifier (DOI)
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