Staudaher, S., Lee, J., & Soleimani, F. (2020, August). Predicting Applicant Admission Status for Georgia Tech's Online Master's in Analytics Program. In Proceedings of the Seventh ACM Conference on Learning@ Scale (pp. 309-312). (Conference Proceedings)

This work reports on progress made towards building an equitable model to predict the success of an applicant to Georgia Tech's Online Master's in Analytics program. As a first step, we have collected and processed data on 9,044 applications and have trained a predictive model with a ROC-AUC score of 0.81, which predicts whether an applicant would be admitted to the program. Our next steps will include using applicant data to model the successful completion of the Analytics program's three core courses, graduation, and finally job placement. In addition, we plan to expand our feature processing and incorporate techniques to ensure that our models do not discriminate based on demographic factors. In the long run, we hope that the results of this study can be used to improve the course contents, selection of offered courses, and prerequisite training, and even give guidance toward the selection of the applicants.