Utilized Decision Tree, Random Forest, XGBoost, and Neural Networks to predict student self-efficacy in Muslim societies. Analyzed factors like self-regulation, problem-solving, and belonging, delivering insights to inform educational interventions and enrich theoretical models of self-efficacy.
Self-efficacy is a critical determinant of academic success, yet its prediction, particularly within Muslim societies, has been underexplored in existing literature. This study addresses this gap by applying machine learning algorithms to predict student self-efficacy, considering not only universal constructs like empathy, forgiveness, and moral reasoning but also those deeply rooted in Islamic values, such as community-mindedness and religious/spiritual beliefs. By investigating these factors, the study provides valuable insights into the socio-emotional and cognitive traits that significantly influence self-efficacy in Muslim educational settings.
In this research, I utilized four machine learning models—Decision Tree (Bagging), Random Forest, XGBoost, and Neural Networks—to predict the self-efficacy of secondary school students in Muslim societies. The model incorporated a comprehensive set of factors, including demographic variables, socio-emotional traits, and cognitive abilities. The study revealed that self-regulation, problem-solving skills, and a sense of belonging were the most influential predictors of self-efficacy, collectively accounting for more than half of the model's predictive power. By delivering data-driven insights, the study not only contributes to the theoretical understanding of self-efficacy but also broadens the application of machine learning in educational research.
My objective in this study was to bridge the gap in existing research by developing a machine learning model to predict student self-efficacy in Muslim societies. Through analyzing a range of factors—including those that are culturally and contextually relevant—I aimed to provide data-driven insights that could inform targeted interventions, enhance educational strategies, and ultimately improve student performance. The findings hold potential implications for policymakers and educators in crafting programs that foster essential skills like self-regulation and problem-solving, thereby improving academic achievement and the overall quality of education in Muslim contexts.