The AEQ-S: A short version of the Achievement Emotions Questionnaire
Bibliographic record
Abstract
The Achievement Emotions Questionnaire (AEQ) is a well-established instrument for measuring achievement emotions in educational research and beyond. Its popularity rests on the coverage of the component structure of various achievement emotions across different academic settings. However, this broad conceptual scope requires the administration of 6 to 12 items per scale (Mdn = 10), which limits the applicability of the AEQ in empirical studies that necessitate brief administration times. We therefore developed the AEQ-S, a short version of the AEQ, with only 4 items per scale that nevertheless maintain the conceptual scope of the instrument. We validated the AEQ-S based on a reanalysis of Pekrun, Goetz, Frenzel, Barchfeld, and Perry's (2011) dataset (N = 389 university students) and by administering them to a new and independent validation sample (N = 471 university students). Despite their brevity, the AEQ-S scales achieved satisfactory reliability and correlated substantially with the original AEQ scales. Moreover, structural relationships and intercorrelations between the scales and their relations with external measures of antecedents and outcomes of achievement emotions were highly similar for the AEQ-S and AEQ scales. These findings suggest that the AEQ-S is a suitable substitute for the AEQ when administration time is limited.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".