Why do instructors pass underperforming students? A Q-methodology study
Bibliographic record
Abstract
BACKGROUND: Formal evaluations are an integral part of a student's learning and encourage students to learn and help instructors identify students' weaknesses. Over the past few decades there have been growing concerns that instructors and evaluators are passing students who do not meet expectations. This phenomenon, in which instructors pass students who do not meet expectations, has been referred to as "failure-to-fail". In this study, we used Q-methodology to identify instructors' justifications for failure-to-fail. METHODS: A Q-methodology study was conducted to identify the major viewpoints of instructors at a Canadian university. A by-person factor analysis with principal component factor extraction and Varimax rotation was used. The analysis was conducted using the QFACTOR program in Stata. A Cohen's effect size of 0.80 was used to identify distinguishing statements. RESULTS: Fifty seven instructors participated in this study. Through a by-person factor analysis, three factors representing three viewpoints emerged: Intrinsically Motivated, Extrinsically Motivated, and Administratively & Emotionally Deterred. The Intrinsically Motivated group perceived mental barriers that prevented them from failing students. They strongly disagreed that they experienced pressure from either students or their schools to pass students. The Extrinsically Motivated believed that their higher-ups and the university encouraged them to pass all students. They perceived discomfort associated with defending their reasons for failing students and were concerned that failing students would damage their own career advancements. The Administratively & Emotionally Deterred group believed that the process of failing a student was stressful and exhausting. They disagreed that a failed student is a result of the instructor's own inadequate guidance or mentorship. CONCLUSIONS: This study identified three distinctive viewpoints that outline areas of consideration for addressing the failure-to-fail mechanism. More transparent discussions within schools, as well as identifying solutions, are required to create systems that ensure educational and professional standards are maintained. Further replication of this study in various disciplines may be used to determine whether these findings are consistent in different fields.
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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 | 0.053 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".