Adapting the Motivated Strategies for Learning Questionnaire for a Writing and Communication Program
Bibliographic record
Abstract
Integrating educational assessment tools such as the Motivated Strategies for Learning Questionnaire (MSLQ) into university classrooms can help students and faculty gain insight into areas of strength and challenge for students. The present study adapted and integrated the MSLQ into a set of first-year communication courses for Faculty of Arts students at the University of Waterloo. This adaptation allowed us to better situate the scale within the writing and communication course context. Through exploratory and confirmatory analysis, a shortened questionnaire (MSLQ-AF) with 6 subscales (motivation, academic self-confidence, performance anxiety, critical thinking, planning for optimal learning, and peer learning) was created. MSLQ-AF proved to have stable factor structure, adequate and stable internal consistency, and construct validity (correlation with grades), when assessed across four samples spanning four university terms. We discuss the role of this new scale in helping students transition into university.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 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".