Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Medical students use a variety of self‐regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear. Objectives This study investigates how self‐regulated learning (SRL) and medical reasoning (MR) occurred concurrently in medical students while completing a diagnostic task in an intelligent tutoring system. This study aims to provide new insights into performance differences between high‐ and low‐achieving students in tasks of varying complexity. Methods Thirty‐one medical students (67.6% female) from a large North American university were tasked with solving two virtual patient cases in an intelligent tutoring system, BioWorld. BioWorld was designed for medical students to practice clinical reasoning skills deliberately. We collected students' think‐aloud protocols, based on which we coded their use of SRL behaviours and medical reasoning activities. We analysed the co‐occurrences of SRL behaviours and medical reasoning activities using the epistemic network analysis (ENA) method. Results The SRL behaviour self‐reflection and MR activity lines of reasoning co‐occurred more frequently in a difficult task than in an easy task. In both tasks, high performers demonstrated more co‐occurrences of self‐reflection and lines of reasoning than low performers. Moreover, the MR activity conceptual operations co‐occurred more frequently with the SRL activities of monitoring and evaluation among high performers compared to low performers in an easy task. Implications The co‐occurrences of SRL behaviours and MR processes account for students' performance differences. The design of computer‐based learning environments for clinical reasoning should promote the acquisition of both SRL and medical reasoning abilities. Moreover, medical educators should consider task complexity when scaffolding.
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.
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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it