Forecasting Success of Computer Science Professors and Students Based on Their Academic and Personal Backgrounds
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916345303531520 |
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| author | Kalhor, Ghazal Bahrak, Behnam |
| author_facet | Kalhor, Ghazal Bahrak, Behnam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02476 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Forecasting Success of Computer Science Professors and Students Based on Their Academic and Personal Backgrounds Kalhor, Ghazal Bahrak, Behnam Computers and Society 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. |
| title | Forecasting Success of Computer Science Professors and Students Based on Their Academic and Personal Backgrounds |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2311.02476 |