Development of a Scalable Self-Regulation Intervention in Support of Lifelong Learning for First-year Engineering Students
Bibliographic record
Abstract
Lifelong learning is a Canadian Engineering Accreditation Board (CEAB) graduate attribute and thus a fundamental component of engineering education, and yet, unlike many core engineering concepts such as thermodynamics or heat transfer, there is no agreed upon approach to teach it to students. This work narrows in on the self-regulation component of lifelong learning and draws on research from the learning sciences, motivation theory, and psychology to develop a scalable intervention for first-year engineering students. Several research-based instructional strategies were modified into four workshops and sets of reminders, each with an emphasis on higher purpose, learning strategies, metacognition, or growth mindset. They were offered to first-year engineering students during the winter term and impact on student performance and beliefs were measured. This thesis presents the details of the workshops, results from their first implementations, two frameworks to evaluate the learning intervention, and suggestions for future work. While this thesis cannot provide conclusive results, it does provide evidence and support that self-regulation can and should be explicitly taught to engineering students.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".