Effects of combining multiple types of adaptive guidance on novices’ knowledge integration of scientific phenomena
Bibliographic record
Abstract
Background: Throughout inquiry learning, many learners, especially novices, experience difficulties and make errors in applying inquiry skills. Because learners’ prior knowledge and difficulties in comprehending concepts and applying inquiry skills are unique, diagnosing learning situations and adapting guidance in real-time is essential. However, effects of combining multiple types of adaptive guidance to support inquiry learning have been understudied. Aims: This study investigated effects of combining primary guidance (just-in-time prompts) and supplementary guidance (hints and confirmatory feedback) on novice learners’ knowledge acquisition and integration of concepts and rules during (i.e., performance success) and after inquiry (i.e., learning outcomes). Sample: The study involved 98 undergraduate students. Methods: In a pretest-posttest design, participants engaged in inquiry tasks to discover the rules of series circuits using a simulation of electric circuits. They were randomly assigned to either a prompt-plus group receiving hints and feedback in addition to prompts, a prompt group receiving prompts only, or a control group receiving no adaptive guidance. The number of rules discovered and the level of knowledge integration during inquiry were assessed. Results: On performance success during inquiry, groups were ordered: 1) prompt-plus, 2) prompt, and 3) control. On the posttest, only the prompt-plus group outperformed the control group. Prior knowledge did not moderate effects of guidance on knowledge acquisition. Conclusions: Coordinated forms of adaptive guidance enhance performance and learning outcomes.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".