A special digital environment to optimize interprofessional collaboration and promote learner engagement
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».