Measuring the Complexity of Self-Regulated Learning and Academic Challenges for Adolescents in Canada
Bibliographic record
Abstract
Research that uses self-report measures to examine the complexity of self-regulated learning (SRL) and academic challenges for adolescents is limited. This study examined the psychometric property of the Self-Regulated Learning Profile and Self-Diagnostic (SRL-PSD) instrument and addressed the multi-components of SRL and academic challenges for adolescents. Participants were 358 adolescents from a Canadian middle school. The subscales of SRL-PSD were administered to students through LimeSurvey during a 25-min instructional session over two days. Results demonstrated the SRL-PSD was a reliable and valid self-report instrument to measure adolescents' SRL practices and academic challenges. Also, all types of SRL practices and academic challenges were significantly intercorrelated. Additionally, all types of SRL practices were positively associated with school engagement, whereas all types of academic challenges were negatively associated with school engagement. Overall, this study provides a validated self-report measure for educators and researchers to examine adolescents' SRL practices and academic challenges.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".