Using Clustering Techniques to Classify Self-Efficacy of Women in Computer Science

The percentage of women majoring in computing fields has fallen dramatically in the past decades, from 35%-37% in the 1980s to about 20% percent in the present. In addition to the low enrollment rate, there is also a large number of female CS students who are dropping out or considering switching their majors. Multiple studies have found self-efficacy to be one of the main barriers that is causing this retention problem. The purpose of this study is to investigate the effects of academic standing, programming experience, and skill level on self-efficacy of female students at the University of Houston, and classify their self-efficacy levels based on their assigned clusters. Clustering models are created using two clustering algorithms: k-means clustering and hierarchical clustering. Finally, we will evaluate the classification accuracy of the model by comparing its outputs with the true outputs obtained from survey results. Results on clustering models show that k-means clustering is superior to hierarchical clustering in terms of cluster analysis and cluster interpretation. Clusters produced by k-means show a linear positive relationship between academic standing, programming experience, and skill level. Evaluation of classification accuracy will be carried out as part of our future work.