Student Engagement and Deep Learning in Higher Education: Reflections on Inquiry-Based Learning on Our Group Study Program Course in the UK.
Bibliographic record
Abstract
A group study program in the UK provides the setting for understanding deep learning in social work education through inquiry-based learning (IBL). Thirteen undergraduate and graduate students from a large university in Western Canada participated in a 15-day learning journey complete with a research methods conference and multiple exchanges with academics, service providers, and service users during their experiential inquiry. Two student coauthors and a faculty member discuss this unique active learning experience in this reflective essay using a constructivist lens to illustrate and make connections between IBL, student engagement, critical thinking, and deep learning. Students’ deep-learning experiences are shared in relation to Sawyer’s (2006) six deep-learning activities, adding to our knowledge about how IBL can support student learning preferences. Implications for consideration for social work education conclude the essay.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".