Making Connections Explicit: The Effect of Self‐explanation on cognitive Integration of Basic and Clinical Sciences in Novices
Bibliographic record
Abstract
Background Several studies have shown that cognitive integration of basic and clinical sciences during learning supports novices' conceptual understanding of disease categories and enhances diagnostic performance over time. It is has been proposed that educational strategies, such as self‐explanation, might promote and support the development of cognitive integration. However, this has not been empirically explored. In this study we examined the relative impact of two basic science instructional approaches (integrated instruction and segregated instruction) and a learning strategy (self‐explanation) on novices' diagnostic accuracy over time. Methods Students from several allied health disciplines (N=71) were taught the clinical features of 4 musculoskeletal pathologies. Students randomly assigned to ‘integrated learning’ (IL) were presented with descriptions that explicitly integrated the clinical features with basic science mechanisms. In the ‘segregated’ (SG) group, the basic science mechanisms and clinical features were presented separately. The self‐explanation (SE) group received the same learning materials as the IL group, but were also prompted to engage in a self‐explanation task immediately after learning each pathology, requiring that they articulate why certain clinical features arose. All participants completed a diagnostic accuracy test immediately after learning and 1‐week later. Results The IL group had superior diagnostic performance compared to the SE (p=0.045) and SG (p=0.011) groups. While diagnostic accuracy declined across all 3 learning groups after a 1‐week delay, the largest drop in performance was observed in the SE group. Conclusion Generating self‐explanations while learning with integrated basic science materials did not positively impact novices' diagnostic performance. We hypothesize that the structure of the self‐explanation task may not have supported the development of a holistic conceptual understanding of each disease. These findings highlight the importance of carefully considering how self‐explanation activities are designed in order to optimize learning of the basic and clinical sciences. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".