Designing Flipped Classrooms to Enhance Learning in the Clinical Skills Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it