Perception of Biology Instructors on Using Student Evaluations to Inform Their Teaching
Bibliographic record
Abstract
Student evaluations of teaching (SETs) provide both summative and formative feedback. Although it is clear that SETs are used by administrators for summative purposes, such as providing data to support personnel decisions, it is uncertain how instructors use them for formative development such as to inform overall teaching practice. The objective of our study was to determine the frequency and nature of SET use for formative purposes, to explore the perception of SET utility to inform teaching practice, and to determine how perception of SET utility might be improved to enhance its use in a formative context. Participants were all biological sciences instructors at a large, research-intensive University. This research was conducted in two phases, using a combination of focus groups, interviews, and a survey to yield both qualitative and quantitative data. We found that while instructors generally perceive that SET feedback has formative utility, and that most instructors have used SET feedback for formative purposes at some point, there are many elements of SET administration that they are dissatisfied with, and they suggest several ways in which SETs could be improved (such as allowing in class time for SET administration or doing multiple administrations per semester) to yield more useable feedback that could inform their teaching. The results of this study can be used to further inform the ongoing debate about the role that SETs should play in higher education, as they demonstrate both the utility and concerns about using SETs for formative purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".