Digital Problem Based Learning for Facilitating the Acquisition of Collaborative Competencies
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
Is Networked Learning (NL) and Problem-Based Learning (PBL) the future of education to foster collaboration? Networked learning has been proposed to be combined with PBL. Both are pedagogies and philosophies influenced by traditions of open learning, radical pedagogies and humanistic educational ideas. Problem-based learning is a constructivist, student-centered, and problem-based approach used at the medical education at Aalborg University (AAU). During COVID, AAU fast-tracked the implementation of Digital PBL (DPBL) methods. Digital PBL opens new possibilities for collaborations across platforms and holds a potential to improve learning. This potential has yet to be released through systematic efforts to understand the possibilities in the context of medical education.The curriculum at the medical education at AAU is centered around the seven roles of physicians originally described in the Canadian framework, CanMEDS. Healthcare systems becomes increasingly complex, interconnected, and rely on interdisciplinary teamwork. The Collaborator role thus becomes more important than ever before. Physicians are expected to excel in clinical expertise and collaborate effectively with other healthcare professionals, patients, and families.The specific focus on collaboration is a good example of alignment between PBL-, NL pedagogy and the CanMEDS framework, and could be important to further exploration using DPBL.This study will investigate the possibilities of DPBL within the medical education at AAU by exploring and testing digitally supported pedagogical design options for facilitating the acquisition of collaborative competencies.This explorative sequential mixed-method study will explore user-needs and perspectives using DPBL in the medical education. This contextualized knowledge will inform the development of a new teaching intervention aimed at enhancing the Collaborator role. It will be evaluated using quantitative measures of student engagement, time requirements, satisfaction and self-report learning outcomes. The data will be merged with focus group interviews to gain an in-depth understanding of the quantitative results.The theoretical background will be learning design, including constructivism. This approach will be employed using the ACAD-model. The DPBL will include a digital element, the podcast media, and a pedagogical practice encouraging the medical students to engage actively in the learning process. This is facilitated by podcast production in groups.At NLC24 the findings of the user-needs and perspectives will be presented and interpreted as part of the design and frame of the podcast intervention. The intervention will take place in autumn 2024 and this can answer if NL and PBL is part of the future education to foster collaboration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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