Post-Secondary Student Evaluations of Teachers: The Debate of Usefulness Continues
Bibliographic record
Abstract
Student evaluations of teachers (SETs) are collected by colleges and universities across the country. Having only been introduced in the early part of the twentieth century, these evaluations are a fairly new tool for higher education administrators to receive feedback and assess the effectiveness of curriculum and instructors. Although implemented as a tool to provide students a medium to share their perspectives, with the goal to improve academic processes, there are concerns regarding their effectiveness, reliability, purpose, and necessity. Further, the literature reflects that students are not well versed by college administrations or faculty members regarding the desired impacts and purpose of SETs, so they are often not completed in a manner that includes cognitive engagement, accurate recall, or the genuine desire to provide constructive feedback and assessment. Even with these limitations, college and university administrators have grown to rely upon SETs to provide constructive insights for instructors to help them improve their teaching effectiveness and summative feedback for committees to use when making promotion, tenure, and compensation decisions. The disconnect between SET objectives and the actual outcomes, however, is problematic. Students often don’t view SETs as impactful, so their level of cognitive engagement is lacking, which can result in skewed, or even false assessments. In fact, since most SETs are completed with the promise of anonymity, they have been used as a weapon by disgruntled students against instructors, regardless of whether the negative feedback is deserved. Finally, SETs have been directly correlated to grade inflation, which has numerous negative implications. The following literature review illustrates the myriad shortcomings of SETs, with the hope that further research will help to discover how they can be re-structured to foster academic excellence in a productive and reliable manner.
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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 | 0.140 | 0.422 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".