The Role of Race and Gender in Teaching Evaluation of Computer Science Professors: A Large Scale Analysis on RateMyProfessor Data
Bibliographic record
Abstract
Recently, Computer Science (CS) education has experienced a renewed interest, driven by the demand in the fast-changing job market. This renewed interest created an uptick of enrollment in computer science courses. Increased number of students search for information about CS courses and professors. Often times, students turn to a professor's profile on online sites, e.g. RateMyProfessor.com (RMP), to read feedback and assessments made by other students. Student Evaluations of Teaching (SETs), conducted online or on paper, are widely used to assess and improve the teaching quality of professors, and to provide critical assessment of the teaching material and content. This paper studies the role of race and gender of computer science professors on their teaching evaluation by analyzing the publicly available data of over 39,000 CS professors on RateMyProfessor. We found that women are generally rated lower then men in overall teaching quality. They are also perceived lower in personality-related student feedback ratings, i.e. they perceived less humorous, and less inspirational. We also found that Asian professors are perceived to be tough graders and lecture heavy. They are also perceived to be more difficult in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".