Exploring perceptions of alternative assessment and grading in graduate anatomy education
Bibliographic record
Abstract
Alternative assessment approaches, such as pass/fail and feedback-based designs, aim to reduce academic stress and foster deeper learning. Few studies have examined feedback-based evaluation in formative settings in medical education, but none among graduate anatomy students. This exploratory study investigated the impact of feedback-based versus quiz-based assessments on graduate students' academic stress, motivation, and perceived learning quality in an anatomy course. Nine students were interviewed to discern perceptions of the impact of the different assessment types. Four instructors were interviewed to establish the philosophies behind their chosen assessment style, and their responses were compared to the perceptions of their students. Inductive thematic analyses of student interviews yielded multiple themes regarding considerations for the influence of assessment on academic-related anxiety and motivation: (1) communicating clear goals and expectations, (2) instructor approachability and individualized assessment, and (3) alignment between perceived effort and outcomes. Faculty interviewees demonstrated intentions to (1) promote non-technical skills, (2) provide authentic experiences, and (3) emphasize individualized assessment. However, there was some misalignment between instructors' goals and students' experiences, exacerbating students' stress and reducing their motivation. Feedback-based assessments were preferred for their perceived individualized nature and facilitation of deeper learning. Findings indicated that implementing feedback-based approaches, while ensuring constructive alignment, could reduce stress for graduate anatomy students. Furthermore, clear communication and instructor approachability can cultivate stronger teaching presence, which may enhance motivation, engagement, and the quality of learning outcomes. Implementing formative feedback-based assessments may, therefore, be an effective strategy to reduce stress and improve learning experiences for graduate-level anatomy students.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".