Comparison of Gender Disparity in Programming Courses using Educational Data Mining

This study explores performance differences between male and female students in programming courses, highlighting distinct learning behaviors using Educational Data Mining (EDM) techniques. We analyzed importance feature for male and female students with SHAP, ELI5, and LIME explainable AI tools. We discover notable variations in learning behaviors between male and female students. Our findings indicate that male students engage more in extracurricular programming activities, such as online problem-solving and contest participation, showing a higher level of involvement in these areas. Conversely, female students exhibit a more structured and disciplined to studying, focusing on consistent academic study and programming practice hours. These insights reveal distinct gender-based learning behaviors, with male students being more active in competitive and practical programming activities, while female students maintain a regular study schedule.