Digital Problem Based Learning for Facilitating the Acquisition of Collaborative Competencies
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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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".