A Decision-Theoretic Tutor for Analogical Problem Solving
Bibliographic record
Abstract
We describe a decision-theoretic tutor that helps students learn from Analogical Problem Solving (APS), i.e., from problem-solving activities that involve worked-out examples. This tutor incorporates an innovative example-selection mechanism that tailors the choice of example to a given student so as to trigger studying behaviors that are known to foster learning. The mechanism relies on a two-phase decision-theoretic process, as follows. First, a probabilistic user model corresponding to a dynamic Bayesian network simulates how a given student will use an example to solve a problem and what she will learn from doing so. Second, this simulation is quantified via an expected utility calculation, enabling the tutor to select the example with the highest expected utility for maximizing learning and problem-solving outcomes. Once an example is presented to a student, the user model generates an assessment of the student’s APS, enabling the selection mechanism to have up to date information on the student. Our empirical evaluation shows that this selection mechanism is more effective than standard selection approaches for fostering learning from APS. Here, we provide a comprehensive technical description of the example-selection mechanism, as well as an overview of its evaluation and a discussion of some of the challenges related to our decision-theoretic approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".