Abstract—Student attrition, a crucial factor in determining institutions’ financial resources, is also a measure of an institution’s academic standing, with a direct impact on its revenue. This study, which examined the factors influencing students’ departure from the Information Systems (IS) program, has significant implications for higher education. The research was conducted in 2 stages. Initially, we experimented with 13 machine learning algorithms to identify the most accurate method for predicting student attrition. Our findings revealed that Extra Trees, Light Gradient Boosting Machine, and Random Forests are the top 3 predictors in that order. In the second stage, we used these algorithms to identify the most influential factors driving student attrition. Our results indicated that the 3 classifiers identified the Last Grade Point Average (Last GPA) as the most significant factor affecting students’ decision to leave the IS program. Performance in mathematics and gender are the other 2 factors affecting dropout. We have found that, as a percentage, more male than female students are leaving the program. Moreover, female students are primarily transferring to more academically demanding majors, such as data analytics and information technology, while male students are transferring to less technology-oriented majors, such as management and law. This finding is reinforced by the observation that female students’ mathematics performance among students leaving the IS program is significantly higher than that of male students. Our results can have significant policy implications for admission procedures and academic advising, positively affecting the IS program’s attrition rate.
 
Keywords—machine learning, random forests, student attrition, extra trees


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