A case study: exploring the impact of 3D printed models on cognitive integration during clinical skills training
Bibliographic record
Abstract
Background: Cognitive integration occurs when trainees make conceptual connections between relevant knowledges and is known to improve learning. While several experimental studies have demonstrated how text and audio-visual instruction can be designed to enhance cognitive integration, clinical skills training in real-world contexts may require alternative educational strategies. Introducing three-dimensional (3D) printed models during clinical skills instruction may offer unique learning opportunities to support cognitive integration. Methods: = 21) worked in small groups and were given access to a 3D printed clavicle, scapula, and humerus. Data were collected through observation, a student focus group, and a semi-structured interview with the instructor. Thematic analysis to review and code the data and to generate themes. Results: We developed four themes that describe how 3D printed models were used in the classroom and how they may support cognitive integration: classroom interactivity, visualization of anatomy, integrating knowledge, and educational potential. Conclusions: The findings demonstrate several ways 3D printed models can augment how learners, instructors, and educational materials interact with one another and how readily learners make connections between different sources and types of knowledge. This research extends previous work by demonstrating how social learning processes and interactions with physical models can offer unique affordances that may support cognitive integration.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".