Preparing medical students as agentic learners through enhancing student engagement in clinical education
Notice bibliographique
Résumé
Preparing medical students to be agentic learners is held to be increasingly important.This is because beyond sequencing, enhancing and varying of experiences across university and health care settings, medical students require epistemological agency to optimize their learning.The positioning of students in these settings, and their engagement with these is central to effective medical education.Consequently, when considering both the processes and outcomes of individuals' learning to become a doctor, it is helpful to account for the interrelated pedagogical factors of affordance, guidance, and engagement.This paper focuses on the last set of concerns -the student's engagement -with particular consideration to how they shape the relations between what experiences are afforded through the medical program and how they elect to engage with them.Evidence from a qualitative study is used to present five salient factors that are central to assist medical students prepare as agentic learners.(Asia-Pacific Journal of Cooperative Education, 2013, 14(4), 251-263) Keywords: Agency, agentic learning, clinical education, personal epistemology, work-integrated learning Educational experiences are only as effective as students' engagement with them; because it is students who elect how effortfully to engage in the learning process and, consequentially, learn.So, beyond what experiences are provided for students by educational institutions (i.e. the enacted curriculum), is how students engage and learn through them (i.e. the experienced curriculum).These provisions include the close personal interactions that students can access (e.g.teacher -student), and the activities made available to assist their learning.Some experiences and interactions will be highly invitational and support individuals' learning whilst, conversely, some might inhibit efforts to learn.For example, in healthcare settings, the close support and guidance of preceptors who want to assist individuals learn and provide authentic opportunities, exercise patience and otherwise support learning are strong and productive affordances.Conversely, when students find themselves being denied access to activities and interactions that are necessary for their learning, productivity will be inhibited.Beyond the quality of these experiences and the degree by which they afford learning, is how students engage with them.This engagement is salient because students learn through active processes of construal and construction of what they experience.Moreover, the intentionality (i.e.personal purpose), effort and direction of their engagement processes are central to their learning.Therefore, students' readiness to take up and engage with the invitations being offered to them is central to their learning.Medical education programs tend to focus on affordances, comprising institutional arrangements (e.g.clinical rotations), deliberate activities to assist their learning (e.g.tutorials, lectures, practicum sessions, access to experts), and ordered processes of affordance and learning (i.e. program structure).However, without considering students' engagement, these provisions alone may be insufficient for effective learning.They have to engage with resources providing access to this knowledge, and negotiate around factors inhibiting the process of accessing it.Students' personal epistemologies, including
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,002 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».