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Record W4226247413 · doi:10.31234/osf.io/bwgk6

The AEQ-S: A short version of the Achievement Emotions Questionnaire

2022· preprint· en· W4226247413 on OpenAlexafffund
Maik Bieleke, Katarzyna Gogol, Thomas Goetz, Lia M. Daniels, Reinhard Pekrun

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of Alberta
FundersUniversität WienUniversity of Alberta
KeywordsPopularityPsychologyScope (computer science)Scale (ratio)Social psychologyPhysicsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.030
GPT teacher head0.349
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes2
Has abstractyes

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