The Tail Wagging the Dog; An Overdue Examination of Student Teaching Evaluations
Bibliographic record
Abstract
Purpose: The purpose of this research is to examine the impact of several factors beyond the professor’s control and their unique impact on Student Teaching Evaluations (STEs). The present research pulls together a substantial amount of data to statistically analyze several academic historical legends about just how vulnerable STEs are to the effects of: class size, course type, professor gender, and course grades. Design/methodology/approach: This research is utilizes over 30,000 individual student evaluations of 255 professors, spanning six semesters, during a three year time period to test six hypotheses. The final sample represents 1057 classes ranging in size between 10 and 190 students. Each hypothesis is statistically analyzed, with either analysis of variance or a Regression model. Findings : This study finds support for 5 out of 6 hypotheses. Specifically, these data suggest STEs are likely to be closest to “5” (using a 1-5 scale with 5 being highest) in small elective classes, and lowest in large required classes taught by females. As well we find support for the notion that higher expected course grades may lead to higher STEs. Practical implications : The practical significance of this research is important. First this research utilized a large data set spanning several years and hundreds of professors and thousands of students and rigorous statistical analysis to assert several important findings. Indeed STEs are impacted significantly by class type, class size, the gender of the professor and the expected course grade. With these findings we suggest a more comprehensive mechanism is in order for evaluation of teaching effectiveness. Social implications: This research could have great social implications if widely read across academic circles. Indeed the tail is wagging the dog; or the student is influencing teaching across America’s universities. It is time to examine teaching effectiveness through a different lens, because using teaching evaluations to determine promotion and tenure, sparse bonus allocation, and teaching awards may be short sighted. Research limitations: While this research is statistically accurate, it is limited by the notion that the data was collected from one large area. As such, care should be taken in generalizing these results to other areas that may have different demographic composition, funding etc. Originality/value: To the best of the authors’ knowledge this research is the first of its kind to statistically analyze such a large body of data and provide a useful guide to help evaluate professors utilizing what information is available.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".