Preparing medical students as agentic learners through enhancing student engagement in clinical education
Bibliographic record
Abstract
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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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".