Connecting students’ descriptions of classroom assessment in higher education with wellness
Bibliographic record
Abstract
Typically, students view classroom assessments like tests, exams, reports, and essays as a major stressor in higher education. However, it is also plausible that students have experienced instances in which the purposes, format, and design of classroom assessment actually supports their well-being. We used a qualitative descriptive design to explore students’ experiences of assessment and wellness. Based on thematic analysis, three low-inference themes emerged all of which showed how any given assessment practice can be experienced differently in terms of student well-being. First, students described how tangible assessment practices including types, grading methods, and assessment design were associated with wellness. Second, participants explained how factors related to their professor such as skills and sensitivity to student stress impacted well-being. Third, students described a wide range of systemic factors associated with assessment that nearly unanimously hindered their wellness. Our results imply that well-being in assessment is a psychological experience that depends on the students’ perceptions more so than the exact assessment practice. Based on our results, we recommend that instructors apply psychological principles known to support well-being to high-quality assessment practices to more predictably influence wellness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".