Predicting Students’ Self-Efficacy in Muslim Societies using Machine Learning Methods (Joining virtually)

The belief in one's own ability to perform a specific task or behavior is referred to as self-efficacy. It is a central concept that correlates strongly and positively with cognitive and behavioral engagement in a given task. In an education based-environment, students’ success is driven by their academic and engagement performance. Students with high self-efficacy also participate more in class, work harder, persevere longer, and have fewer negative emotional reactions when faced with difficulties compared to students with low self-efficacy. The goal of this study is to investigate and understand students' self-efficacy in Muslim societies. To achieve the goal of this study, factor analysis is performed to understand the underlying structure of a survey dataset followed by developing predictive models using machine learning methods. This study seeks to predict student self-efficacy (SSE) using a set of factors that represent different dimensions, such as psychological competencies, values, socioeconomic, and religious predictors.

The survey dataset—used in this study—was collected and provided by the International Institute of Islamic Thought as part of an initiative on advancing education and human development in Muslim communities. There is a lack of studies on students' self-efficacy in Muslim societies, which have unique values systems and face critical development challenges. The outcome of this study will further advance the body of knowledge in educational research and potentially benefit researchers and educators with a focus on Muslim societies. Academic and other educators from other societies could also use the findings of this study to identify students with low or high levels of self-efficacy and to develop self-efficacy improvement interventions.

Mohammed Alrezq is a PhD student in the Grado Department of Industrial and Systems Engineering at Virginia Tech. Alrezq received his B.S. and M.S. in Industrial Engineering. His research interests include the methodologies and applications of continuous improvement, data analytic/science, and modeling.

Mohammed Ba-Aoum is a Virginia Tech PhD candidate in Industrial and Systems Engineering. He earned dual master's degrees in Social Pedagogy and Industrial Engineering from Arizona State University. His research focuses on using data analytics, systems thinking, and simulation modeling to develop high-leverage policies and enhance institutions' effectiveness. Furthermore, he is interested in envisioning and strategizing ways to improve education systems by advancing integrative knowledge and enhancing interdisciplinary research, particularly in engineering and social science. Mohammed worked as a lecturer at King Fahd University of Petroleum and Minerals and as an engineer in ARACMO before joining Virginia Tech.