Forecasting Success of Computer Science Professors and Students Based on Their Academic and Personal Backgrounds
Ghazal Kalhor, Behnam Bahrak
TL;DR
The paper investigates how the prior universities of CS students and their advisors influence admission to top North American programs and future academic success. It combines descriptive distributions, correlation analyses including Kendall’s tau, hypothesis testing for advisor bias, network analysis of prior universities, and machine learning to predict professors’ $h$-indices using 28 predictors with 5-fold cross-validation, achieving RMSE $=7.89$ for the neural network. Key findings include a positive relation between prior-university prestige and admissions ($\tau=0.4244$, $p=0.0010$), a majority of faculty with PhDs from top-25 institutions ($p$-value $=3.56\times10^{-39}$), and a bachelor’s bias in admissions. The results highlight implications for admissions fairness, diversity, and forecasting scholarly impact in CS academia.
Abstract
After completing their undergraduate studies, many computer science (CS) students apply for competitive graduate programs in North America. Their long-term goal is often to be hired by one of the big five tech companies or to become a faculty member. Therefore, being aware of the role of admission criteria may help them choose the best path towards their goals. In this paper, we analyze the influence of students' previous universities on their chances of being accepted to prestigious North American universities and returning to academia as professors in the future. Our findings demonstrate that the ranking of their prior universities is a significant factor in achieving their goals. We then illustrate that there is a bias in the undergraduate institutions of students admitted to the top 25 computer science programs. Finally, we employ machine learning models to forecast the success of professors at these universities. We achieved an RMSE of 7.85 for this prediction task.