Development of lifelong learning strategies using inquiry guided learning projects in a first year anatomy course (211.6)
Bibliographic record
Abstract
Biomedical science courses are often crammed with mandatory content. As a result, students may develop passive learning strategies; relying on the instructor to ‘give them’ knowledge. For instance, students self‐report (pre‐semester survey, 10‐pt Likert) a lack of confidence in the exploration of research on a scientific topic (6.4 ± 1.1) outside of formal instruction. To evade this passivity and to promote lifelong learning strategies, Inquiry Guided Learning Projects (IGLPs) have been developed for first year Gross Anatomy; piloted in 2012, then evaluated/reformatted and integrated in 2013. Aligned with student desires to develop skills in effective communication (8.1 ± 1.8) and resource acquisition (8.5 ± 1.9) IGLPs facilitation include 4 novel Information Sessions and 3 Check‐Ins that guide students through the Information Search Process (ISP); initiation, selection, exploration, formulation, collection, and presentation. The guiding aspect was imperative, as students indicate poor self‐perceived abilities of research question selection (6.6 ± 1.7), answer exploration using peer‐review literature (6.4 ±1.2), and effective scientific communication (6.3 ± 1.5). The effect of IGLPs on change over time in student‐perceived confidence and self‐reported ability in all ISP stages will be tested with a repeated measures ANOVA. IGLPs prepare students to be active learners; confident in their ability for academic discovery following formal education. Grant Funding Source : Supported by CIHR CGS Doctoral Research Award
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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 | 0.004 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".