Gathering validity evidence in the development of a new version of the Attitude toward the Subject of Chemistry Inventory (ASCI-UE)
Bibliographic record
Abstract
Organic chemistry is one of the most feared and failed courses due to its complex and fast-paced nature. Investigating affective metrics that relate to achievement in these courses can be worthwhile, particularly when these metrics show predictive relationships to achievement. Attitude toward chemistry has been investigated utilizing a variety of instruments. We present herein an instrument related to the well-established Attitude toward the Subject of Chemistry Inventory (ASCIv2). This new instrument was developed utilizing The Standards of Psychological and Educational Measurement, which describe five aspects of validity evidence that should be gathered when using instruments in research. Content validity was gathered through consultation with experts. Response process validity was gathered through student interviews. Internal structural validity was collected through confirmatory factor analysis. Relations to other variables were investigated through correlation analysis and structural equation modeling. Consequential validity was studied through measurement invariance testing before comparing scores for subgroups (high- and low-achieving students). Additionally, reliability evidence was tested with Omega coefficients for each of the factors. We showed that all tests and analyses were done with high rigor, and that this new instrument, ASCI-UE, measuring utility and e motional satisfaction, can provide interesting results and implications for research and practice.
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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 | 0.142 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".