Effects of just-in-time inquiry prompts and principle-based self-explanation guidance on learning and use of domain texts in simulation-based inquiry learning
Bibliographic record
Abstract
Although scientific inquiry with simulations may enhance learning, learners often face challenges creating high demand for self-regulation due to an abundance of information in simulations and supplementary instructional texts. In this research, participants engaged in simulation-based inquiry about principles of electric circuits supplemented by domain-specific expository text provided on-demand. They received just-in-time inquiry prompts for inquiry behaviors, guidance to self-explain electrical principles, both, or neither. We examined how these interventions influenced participants’ access of text information and achievement. Undergraduates ( N = 80) were randomly assigned to one of four groups: (1) inquiry prompts and principle-based self-explanation (SE) guidance, (2) inquiry prompts without principle-based SE guidance, (3) principle-based SE guidance without inquiry prompts, or (4) control. Just-in-time inquiry prompts facilitated learning rules. However, there was no main effect of principle-based self-explanation guidance nor an interaction involving both interventions. Effects of just-in-time inquiry prompts were moderated by prior knowledge. Although principle-based self-explanation guidance promoted re-examination of text-based domain information, reading time did not affect posttest scores. These findings have important implications for instructional design of computer-based adaptive guidance in simulation-based inquiry learning.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".