From Example Studying to Problem Solving via Tailored Computer-Based Meta-Cognitive Scaffolding: Hypotheses and Design
Bibliographic record
Abstract
We present an intelligent tutoring framework designed to help students acquire problem-solving skills from pedagogical activities involving worked-out example solutions. Because of individual differences in their meta-cognitive skills, there is great variance in how students learn from examples. Our framework takes into account these individual differences and provides tailored support for the application of two key meta-cognitive skills: self-explanation (i.e., generating explanations to oneself to clarify studied material) and min-analogy (i.e., not relying too heavily on examples during problem solving). We describe the framework’s two components. One component explicitly scaffolds self-explanation during example studying with menu-based tools and direct tailored tutorial interventions, including the automatic generation of example solutions at varying degrees of detail. The other component supports both self-explanation and min-analogy during analogical problem solving by relying on subtler scaffolding, including a highly innovative example selection mechanism. We conclude by reporting results from an empirical evaluation of the former component, showing that it facilitates cognitive skill acquisition when students access it at the appropriate learning stage.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".