Developing & Evaluating Collaborative Medical Physics Module for the First Year Medical Students at College of Medicine & Medical Sciences, Arabian Gulf University Kingdom of Bahrain
Bibliographic record
Abstract
Collaborative learning is emerging as an important learning method. It is an educational approach for teaching and learning; that involves groups of learners working together to solve a problem, complete a task, or create a product. This paper describes a comprehensive approach in collaborative inquiry of medical physics at College of Medicine & Medical Sciences (CMMS) Arabian Gulf University (AGU). The collaborative module comprises: an interactive medical physics WebCT virtual learning environment that provides students with shared workspaces for coordinating and recording their collaboration in scientific inquiry; inside and outside field visits carried out collaboratively by each subgroup and the tutor. Medical physics diagnostic and application dialogue (learning problems) and Web-based materials are designed to match and enrich the module. The individual and group assessments given to students guide their learning process, and help them to scientifically report and evaluate their collaboration inquiry experiences. The main aim of this work was to redesign the medical physics module at the AGU and contribute in shifting the learning process from a teacher-center to a learner-center activities and support learner-learner interaction, learner-content interaction, and learner-tutor interaction to a degree that facilitate deep learning and fulfill satisfaction with learning. The results indicate that collaborative learning enabled the participants to communicate easily with their teachers (resource people, tutors and professors) and their peers searching for answers for themselves. In addition, the participants were able to assess their own expertise, resulting in the enhancement of knowledge, skills, attitudes and satisfaction with learning. Concerning achievement in medical physics; data analysis results revealed no significance differences related to treatment type (collaboration, no collaboration) or the gender of the experimental group participant`s (male, female). A remarkable result was that participants who were taught through collaborative approach scored significantly more gain in achievement (M = 15.1289, SD = 16.84061) than the control group that did not use collaborative approach (M = 6.1225, SD= 21.26310), t(290) = -4.023, p < .05. i.e. collaborative approach for teaching medical physics prove its strength in empowering subjects gain development in achievement. Further research on more courses is needed to cross validate the study findings and generalize the results. Attached an appendix titled “X-Ray and Medical Diagnostic Dialogue”.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".