Using a multitrait‐multimethod analysis to examine conceptual similarities of three self‐regulated learning inventories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A programme of construct validity research is necessary to clarify previous research on self-regulation and to provide a stronger basis for future research. AIM: A multitrait-multimethod (MTMM) analysis was conducted to assess convergent and discriminant validity of three self-regulation measures: the Learning and Study Strategies Inventory (LASSI; Weinstein, 1987), the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1993) and the Meta-cognitive Awareness Inventory (MAI; Schraw & Dennison, 1994). Method bias across all three inventories was also examined. SAMPLE AND METHOD: Three hundred and eighteen undergraduate university students (255 female, 61 male, 2 did not specify) were recruited from various courses to participate in research on perceptions about studying and study methods. Participants spent 30-60 minutes completing all three inventories. RESULTS: Evidence for convergent validity was found at the matrix level, but was attenuated when examined at the individual parameter level. Evidence for discriminant validity among traits was modest, and common method bias was evident across all three measures. CONCLUSIONS: Results revealed the three inventories yielded different results, which suggests that researchers should be selective in the inventory they use to assess self-regulated learning (SRL).
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it