Scientific inquiry learning with a simulation: providing within-task guidance tailored to learners’ understanding and inquiry skill
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
In scientific inquiry learning, within-task guidance tailored to the learner’s domain knowledge and inquiry skill may be essential to promote intended learning outcomes. However, due to dynamic complexity across the timeline of inquiry learning, principles for designing tailored guidance are elusive. In this study, experienced tutors provided just-in-time guidance to 11 learners. We analysed tutor-learner interactions to investigate how tutors adapted guidance. We found tutors provided five types of guidance: prompts, support for domain knowledge, assessments, hints, and feedback. Guidance was provided when learners made errors, expressed difficulties, or asked questions; or when the tutor judged a learner successfully demonstrated a skill and was ready to progress to a follow-on skill. Based on these results, we propose a model for tailored, just-in-time guidance in simulation-assisted inquiry learning environments.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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