Exploring Student Engagement, Confidence, and Usefulness for Female Students in CS Class at High School Using Machine Learning

Females remain underrepresented in computer science (CS), despite numerous studies investigating the causes using different data types and analysis techniques. In recent years, machine learning (ML) has been increasingly used in education, particularly for analysing student engagement using some engagement dimensions. However, no study has yet used ML algorithms to analyse student behavioural, cognitive, emotional, and social engagement survey data. In this paper, we present a study investigating whether these four dimensions of engagement are related to female high school students' beliefs in the usefulness of learning computer science and their confidence in taking CS classes in Saudi Arabia. We also employ ML techniques to identify important indicators that can predict students' confidence and beliefs in the usefulness of learning CS. Additionally, we compare three supervised ML techniques, Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), to determine which algorithms better predict confidence and usefulness in learning CS. Our sample consisted of 284 participants from four schools who completed the multidimensional survey, and we evaluated the ML algorithms using Mean squared error (MSE), Mean absolute error (MAE), and Determination coefficient (R2). Our findings show that each dimension of student engagement positively correlates with the confidence and usefulness of learning computer science, and it is possible to predict them from student engagement indicators. The RF model outperformed DT and LGBM, identifying 'enjoyment in learning new things' and 'interest in topics in CS class' out of 28 indicators as the most important features to predict usefulness. Additionally, from 28 features, 'looking forward to CS class' and 'enjoyment in learning new things' are the most important indicators influencing confidence. Our findings contribute to a broader understanding of student engagement in CS education and highlight various indicators used to measure student engagement. These findings shed light on factors that may motivate and interest female students toward CS learning. Future work will use a larger sample of participants from schools and higher education in different countries.