A special digital environment to optimize interprofessional collaboration and promote learner engagement
Bibliographic record
Abstract
As part of a project funded by the Social Sciences and Humanities Research Council (SSHRC), we would like to implement a module for use in conjunction with Moodle, with the goal of optimizing interprofessional collaboration and promoting learner engagement. \n \nThis project is based on the fact that clear adaptation and flexibility needs in education have been identified. Health sciences education is one example, particularly in the context of complex impairments (such as chronic pain, long COVID or mental health issues), where these needs are at the core of clinical and educational challenges. Such a context calls for interprofessional practice and requires taking into account different professional cultures and practice settings, in addition to the patient’s life circumstances, literacy level and culture. Learners, whether they are patients or informal caregivers, and teachers must develop adaptation skills and equip themselves to consider diverse contexts and cultures. Patients and their loved ones will go through a learning path (from disease and self-management of this disease and including any care that may be considered), at the same time as or before starting on their care pathway, to enable them to engage as full participants in their care. It is our aim to give them the necessary tools to do so. The use of digital learning environments (DLEs), which eliminates the constraints of time and geography, provides opportunities to demystify all that is involved in this process, and opens up a wealth of learning opportunities. These DLEs thus provide special access to a diverse array of contexts and cultures. But how can their full pedagogical potential be harnessed? \nWe contend that learner engagement and interprofessional collaboration are necessary. In order to foster these, we propose first to identify knowledge about cultures and contexts, as this is often implied and can lead to misunderstandings. Next we propose to organize it in such a way that DLE users will be equipped to mobilize this knowledge. For this purpose, we will develop and evaluate computerized tools for leveraging this knowledge, engaging learners and optimizing interprofessional collaboration practices. These tools will be brought together in a module used in conjunction with the Moodle environment: the SPÉCIAL module, which stands for Scénarisation PÉdagogique Collaborative Intégrant des Alternatives et des Liens [Scripting that is PEdagogical [and] Collaborative Integrating Alternatives and Links]. The links developed are those between 1) learning data and knowledge that has been accumulated and updated, in particular through artificial intelligence (AI) techniques, 2) pedagogical tools (DLEs, portfolios, etc.), and 3) the environments where the users are found. \n \nProject team: \nPrincipal investigator (P.I.): Isabelle Savard \n \nCo-investigators: \nPatrick Plante – TÉLUQ University \nGustavo Angulo - TÉLUQ University \nDaniel Lemire - TÉLUQ University \nJean-Sébastien Roy - Laval University \nKarine Latulippe – McGill University \nLuc Côté - Laval University
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".