Designing Flipped Classrooms to Enhance Learning in the Clinical Skills Laboratory
Bibliographic record
Abstract
Flipped classroom is an educational technique in which content is delivered online for students to study at their own pace in preparation for in-class learning. Benefits include increased flexibility, enhanced student engagement and satisfaction, and more effective use of time spent during face-to-face teaching. However, the development and implementation of flipped classroom teaching are also associated with challenges, including time required to develop learning materials and getting students to engage with the preparatory work. This teaching tip describes a structured approach to designing and implementing the flipped classroom approach for clinical skills to allow a greater focus on practicing the hands-on skills and the provision of feedback during the laboratory session. First, the rationale for flipping the classroom and the expected benefits should be considered. On a practical level, decisions need to be made about what to include in the flipped component, how it will complement the face-to-face class, and how the resources will be created. In the design phase, adopting a structured template and aligning with established pedagogical principles is helpful. A well-designed flipped classroom motivates learners by including different elements such as quality educational media (e.g., videos), the opportunity to self-assess, and well-defined connections to relevant knowledge and skills. Student engagement with the flipped material can be promoted through different strategies such as clear communication to manage student expectations and adapting the delivery of the face-to-face component. Finally, gathering feedback and evaluating the initiative are important to inform future improvements.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".