A microanalysis of learner questions and tutor guidance in simulation‐assisted inquiry learning
Bibliographic record
Abstract
Abstract Background Guidance during inquiry learning plays an important role in developing conceptual understanding and inquiry skills. This study analysed learner‐tutor interactions in a simulation‐assisted learning environment to investigate how tutor guidance enabled knowledge construction and fostered epistemic practice. Objectives This research aimed to illuminate challenges learners encounter in the inquiry process and forms of guidance that support learning in both conceptual and epistemic aspects. Methods This study uses a mixed methods approach. We analysed video recordings in which nine participants asked 72 questions and the microsequences of interactions immediately surrounding and including each question. We coded properties of each question and whether the tutors' utterances were intended to increase (upregulate) or decrease (downregulate) the complexity of the inquiry processes, and used a two‐step cluster analysis to explore groupings emerged from tutors' regulation guidance and learners' questions. Results and Conclusions The regulatory intent of tutors' utterances depended on various characteristics of student questions. The microsequences clustered in five categories: 1) upregulated investigation and inference, 2) upregulated evidence‐based justification, 3) downregulated cognitive load, 4) downregulated procedural uncertainties, and 5) downregulated perceptual dissonance. Our findings suggest tutors offering guiding prompts should consider dual processes in the inquiry and, by strategically prompting them, strike a balance between the goals of guiding learners to discover scientific knowledge and grounding their conceptual understanding in concepts, data, and procedures. Implications We emphasize conceptual and epistemic learning should be concurrently guided in scientific inquiry. We propose a bidirectional guidance model as a pedagogical approach to guide instructional practice.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".