University students’ negative emotions in a computer-based examination: the roles of trait test-emotion, prior test-taking methods and gender
Bibliographic record
Abstract
Although the effectiveness and experiences of computer-based examinations is a widely investigated area of research, the question of whether and how computer-based assessment limits or heightens the experience of negative test emotions remains largely unexamined. Drawing from the control-value theory of achievement emotions, we investigated undergraduate students’ emotions during an authentic, course-based assessment in a computer-based testing environment, as well as predictors and outcomes associated with their emotions. We found that students (N = 74) in a computer-based testing environment reported lower levels of negative emotions than their typical negative test emotions. Females and males performed equally in the examination, yet females reported higher retrospective negative emotions. Consistently, females reported higher levels of typical test-taking anxiety in prior examinations, but they reported lower anxiety in a computer-based environment. Finally, although typical and retrospective emotions were correlated, only retrospective emotions were associated with examination performance. We discuss the importance of testing environments and time-frames in understanding how to support students’ emotions in testing with particular emphasis on implications for online assessment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".