Utilisation des TIC: adaptation et validation de trois echelles de mesure de variables affectives
Bibliographic record
Abstract
À partir des travaux de Gardner (1985) réalisés en langues secondes et de son questionnaire d'attitudes et de motivation, trois échelles de mesure – soit les attitudes, la motivation et l'anxiété – ont été adaptées à une situation impliquant l'utilisation des TIC. La présente étude décrit les étapes d'adaptation et de validation de ces échelles. Les données ont été recueillies à deux reprises auprès des mêmes étudiants pour former une seule base de données. Les résultats obtenus des analyses de validité interne laissent voir l'importance de différencier les TIC en tant que source d'information, de l'ordinateur comme outil de travail. Les résultats de l'analyse factorielle font ressortir la proximité des concepts d'attitude et de motivation ainsi que la distinction de celui d'anxiété. Des suggestions sont émises pour améliorer les présentes échelles. Pour l'enseignant de L2 sensible à l'influence des variables affectives, cette étude fournit des échelles utilisables auprès d'apprenants ayant recours aux TIC. Based on second language research by Gardner (1985) and his questionnaire for measuring attitudes and motivation, three scales pertaining to attitudes, motivation, and anxiety were adapted for use in a context involving information and communication technologies (ICTs). The present study describes the stages involved in adapting and validating these scales. The data were collected from the same students at two different times to create a single database. The results from analyses of internal validity suggest the importance of distinguishing between ICT use as a means of obtaining information and the tool itself. Factor analysis reveals that the constructs of attitude and motivation are highly related, whereas anxiety emerged as distinct. Recommendations are formulated concerning improvements to the present scales. For the L2 instructor aware of the importance of affective variables, this study provides scales that can be used with learners in an ICT context.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".