The Evaluation of an Integrated Growth & Goals Module to Better Equip Students with Learning Skills in Postsecondary Courses: Systematic, Scalable, and Explicit
Bibliographic record
Abstract
Objective: Most students spend years in formal education settings without being explicitly taught how to learn effectively. Our objective was to evaluate an innovative intervention designed to effectively equipping all students with learning skills, called the Growth & Goals Module, which is an adaptable open education resource available in English and French. Methods: We evaluated the module using a Practical Participatory Evaluation approach and the 4-level Kirkpatrick Evaluation model. To investigate ten research questions aligned with the model, we collected data from 1845 students and five educators from nine undergraduate courses in science, engineering, and mathematics through questionnaires, focus groups, course assessments, and institutional data. Results: Students and educators reported high satisfaction (Level 1, Learning). The training was new to most students and most completion rates were over 75% when educators provided an incentive. Students in some demographics used the module less than others. In Level 2 (Learning), students’ metacognitive skills increased. They could identify SMART goals and differentiate growth/fixed mindset statements. At Level 3 (Behaviour), students reported intending to use the module in the future. Most educators created learning outcomes for the first time. The module required little time of students and educators; however, greater support, incentives, and rewards are needed for project sustainability. Educators have used the module in courses in many disciplines and levels (Level 4, Results). Conclusions: The Growth & Goals module explicitly teaches core learning skills for students in science, engineering, and mathematics courses and has the potential to scale to other disciplines and levels.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".