12. “The Case of the Castle”: An Application of Experiential Learning at the Bader International Study Center (BISC) in Medical Education
Bibliographic record
Abstract
It has been internationally recognized that medical education in higher-education institutions must adapt to equip healthcare professionals with skills that meet the changing demands of patient-cantered care (General Medical Council, 1993). Experiential learning is a teaching strategy based on Kolb’s Learning Theory (KLT) that encourages learners to proactively consolidate information through practical applications, self-directed reflection, and critical appraisal of evidence. Based on a descriptive phenomenological research design, the lived-experiences of both students and faculty at the Bader International Study Center were analyzed qualitatively through observation and interviews, and quantitatively through Likert scale surveys. Conditions identified by previous publications as conducive to experiential learning were tested, and thematic and factor analysis were conducted respectively on subsequent data. Results of the analysis found that students consolidated information best when new knowledge presented built upon their prior understandings of the phenomenon. Thorough understanding of learning goals prior to the experiential learning opportunity encouraged learner ownership, and subsequently facilitated self-directed, professor-facilitated learning. Retention of information was highest when students were presented with reflection assignments that prompted them to critically apply prior knowledge in a low-risk environment. The implications from experiential learning for the advancement of medical education therefore can be summarized in the following points: learner-directed instead of professor-directed PBL scenarios may encourage learner responsibility and increase knowledge retention. Encouraging active reflection and subsequent experimentation through PBL and in realistic ‘life scenarios’ may increase transferability of professional skills as dictated by the General Medical Council.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".