LEARNING FROM THE INTEGRATED CURRICULUM APPROACH: STUDENT REFLECTIONS DURING AND AFTER THEIR EXPERIENCE
Bibliographic record
Abstract
At the University of Calgary, we piloted an integrated curriculum approach in second-year electrical engineering. The intention was to provide authentic learning experiences, with the ultimate goal of fostering deep learning in the students. To improve students’ learning strategies, they were asked to reflect weekly on their learning during the program (Winter 2019: Jan-Apr), and during their first semester of third-year, which was run in the traditional format (Fall 2019: Sep-Dec). Using qualitative coding, these reflections were analyzed with a framework from self-determination theory to understand the student learning and motivation throughout the program. There were 11 themes that emerged, categorized within the three elements of the theoretical framework: competence, relatedness, and autonomy.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".