Online case-based learning in medical education: a scoping review
Bibliographic record
Abstract
BACKGROUND: Case-Based Learning (CBL) in medical education is a teaching approach that engages students as learners through active learning in small, collaborative groups to solve cases from clinical patients. Due to the challenges afforded by the COVID-19 pandemic, small group learning such as CBL, transitioned quickly to include technology-enhanced learning to enable distance delivery, with little information on how to apply pedagogical frameworks and use learning theories to design and deliver online content. METHODS: To extend understanding of online CBL a scoping review protocol following the PRISMA-ScR framework explored the literature that describes the use of online CBL application in medical education and the outcomes, perceptions, and learning theories. A literature search was conducted in January 2022 followed by a subsequent review in October 2022. After peer review using the PRESS guidelines, the CASP appraisal tool was used to assess the rigor of each study design. RESULTS: The scoping review identified literature published between 2010 and 2022 (n = 13 articles), on online CBL in the field of medical education with 11 observational studies describing student and facilitator perceptions and two randomized controlled studies. Positive perceptions of online learning included a flexible work-life balance, connection with learners, and improved accessibility. Negative experiences of online CBL included poor internet access, a distracting learning environment, and loss of communication. In the studies that collected student performance data, results showed equivalent or improved outcomes compared to the control. The CASP appraisal tool highlighted the deficiencies in most study designs, lack of framework or learning theory, and poor reproducibility of the methods to answer the research questions. CONCLUSION: This scoping review identified literature to describe the academic outcomes, and student and facilitator perceptions of online CBL in medical education. However, the CASP tool uncovered deficiencies in study descriptions and design leading to poor quality evidence in this area. The authors provide recommendations for frameworks and learning theories for the future implementation of online CBL.
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.
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 | 0.037 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.041 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 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".