Étude de la validité d’un instrument de mesure de la compétence informationnelle : l’exemple du QuizCI
Bibliographic record
Abstract
This research documents the validity of QuizCI, an instrument for measuring information literacy (CI). The QuizCI was developed at the Université du Québec en Outaouais, a French-language university in Canada. The instrument has 32 multiple choice items. Evidence of validity is documented according to the types of validity Laveault and Gregory (2002). To support the development of QuizCI, arguments of apparent and content validity were collected. With the instrument developed, a sample of data from 469 students was collected from 2012 to 2015. This sample was used to perform analyzes and produce information intended to document the conceptual validity of QuizCI. Among the information taken into account are the simplicity of index, the discrimination, the Cronbach's alpha, the exploratory factor analysis solution available information after the application of a Rasch. After which, the study concludes that the QuizCI measurement IC with some validity, but also the finding various problems affecting the measurement. Some items are problematic. Then, the whole instrument is easy for students, which affects the measurement. Thus, this first portrait of the validity of QuizCI is a starting point for improvement. Finally, not many authors present study of the validity of an instrument to measure Information Literacy, especially in French-speaking context. Also, this study may provide an example to use of these methods.
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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 | 0.155 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".