A Canadian Investigation of the Psychometric Properties of the Student Motivation and Learning Strategies Inventory
Bibliographic record
Abstract
The psychometric properties of the Student Motivation and Learning Strategies Inventory (SMALSI) were examined using a sample of 404 Grade 6 students from an urban Canadian school system. Students completed the SMALSI and school factors included final school grades, attendance records, and language arts, mathematics, science, and social studies results from Provincial Achievement Tests (PATs). Confirmatory factor analysis of SMALSI demonstrated less than adequate fit for each individual SMALSI factor though with some covariance of similar items, the model fit approached acceptable limits for most factors. Results generally confirmed that the SMALSI subscales were significantly related to all of the achievement variables including PAT results and final school grades. Structural equation modeling demonstrated that writing and research skills, test-taking skills, low motivation, and test anxiety all contributed to the prediction of PAT results. Test anxiety was a significant predictor of achievement across all subject areas. Canadian Grade 6 students demonstrated lower motivation, less test anxiety, and fewer attention problems but were otherwise comparable with the U.S. sample. Results provide convergent evidence supporting the psychometric properties of the SMALSI with a Canadian sample; however, there is some room to improve the overall model fit in subsequent revisions of this measure.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".