USING K-NEAREST NEIGHBORS TO CLASSIFY UNDERGRADUATE FEMALE SELF-EFFICACY IN COMPUTER SCIENCE

Abstract

Since the introduction of new curriculum standards in high school, computer science is increasing in interest amongst incoming first-year undergraduate students. However, student retention rates, especially female undergraduate students, in computer science, are among the lowest among all STEM majors. Therefore, this research aims to assess the relationship between computer science and programming self-efficacy among female STEM major and minor students. We will use the results to help in the development of supplemental resources for undergraduate female students. Throughout this study, the information will be collected to develop a classification-based machine learning algorithm. A focus group will be conducted to gather more input from the students on their computer science educational experience.Furthermore, we will use the results to help develop supplemental resources for undergraduate female students. The findings will be used to investigate and improve areas of concern for female undergraduate students. Since self-efficacy is a product of self-belief and engagement, this supplemental support will help stakeholders such as instructors, universities, and companies to generate suitable strategies to address the issue and support female computer science undergraduate students in their journey to become computing professionals.