Factors related to the likelihood of grade inflation at community colleges
Bibliographic record
Abstract
A number of studies have documented a trend of higher grades awarded by postsecondary institutions in both the United States and Canada over the last two decades. Grade inflation in higher education is a potentially costly problem for a variety of reasons, but little empirical research about the causes of grade inflation has been conducted. This study investigated multiple potential factors related to the likelihood of grade inflation by faculty members at community colleges. These factors included perceptions of student evaluations of teaching (SETs), perceptions of job security, perceptions of student complaints, experience with grading practices, perceptions of student nuisance, and instructors’ empathy with students. Additionally, the possibility that factors related to the likelihood of grade inflation influence adjunct and full-time instructors differently was tested. An electronically-distributed survey was employed to measure the perspectives of 336 instructors at seven community colleges in three states. Complex models did not predict likelihood of grade inflation, but differences were found between some factors for instructors in high and low likelihood of grade inflation groups. Instructors in the low likelihood group perceived higher levels of both student complaints and student nuisance than instructors in the high likelihood group. Faculty status was found to affect the influence of perceptions of student evaluations of teaching (SETs), perceptions of job security, perceptions of student complaints, and experience with grading practices on likelihood of grade inflation. The results of this study suggest that additional research should elucidate the potential connections between instructors’ perceptions of student nuisance and student complaints and the phenomenon of grade inflation. Furthermore, additional work is needed to determine what effects SETs have on instructors’ careers and the perceptions of instructors regarding those effects. The results of this study potentially inform the practice of using faculty professional development to educate instructors about the process of grading. Specifically, institutions should explicitly define the intended functions of grades prior to establishing a system for determining grades. These institutions should also provide guidance to instructors so that all agents in the grading process are using these symbols of student performance in a consistent manner. Faculty should engage in the conversation about appropriate functions of grades and more consistent methods for determining grades. Finally, administrators should exercise caution in the interpretation of feedback from students in the forms of student evaluations of teaching and student complaints, particularly as used in the supervision of adjunct instructors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".