Does Case-based blended-learning expedite the transfer of declarative knowledge to procedural knowledge in practice?
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
Abstract Background Case-Based Learning (CBL) has seen widespread implementation in undergraduate education since the early 1920s. Ample data has shown CBL to be an enjoyable and motivational didactic tool, and effective in assisting the expansion of declarative and procedural knowledge in academia. Although a plethora of studies apply multiple choice questions (MCQs) in their investigation, few studies measure CBL or case-based blended learning (CBBL)-mediated changes in students’ procedural knowledge in practice or employ comparison or control groups in isolating causal relationships. Methods Utilizing the flexibilities of an e-learning platform, a CBBL framework consisting of a) anonymized patient cases, b) case-related textbook material and online e-CBL modules, and c) simulated patient (SP) contact seminars, was developed and implemented in multiple medical fields for undergraduate medical education. Additionally, other fields saw a solo implementation of e-CBL in the same format. E- cases were constructed according to the criteria of Bloom’s taxonomy. In this study, Objective Structured Clinical Examination (OSCE) results from (n=619) medical students in 2013 before CBBL implementation, and after CBBL implementation in 2015 (n=624) and 2016 (n=643) were analyzed. Results A significant improvement (adjusted p=.002) of the mean OSCE score by 1.02 points was seen between 2013 and 2015 (min=0, max=25). Conclusion E-Case-Based Learning is an effective tool in increasing student satisfaction, improving performance outcomes and may provide a sustainable learning platform for many fields of medicine in future.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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