Augmenting the novice-expert overlay model in an intelligent tutoring system: Using confidence-weighted linear classifiers
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 BioWorld, a medical intelligent tutoring system, novice physicians are tasked with solving virtual patient cases. Whilst the importance of modeling and predicting clinical reasoning is recognized, an important aspect of the learner contribution remains unexplored - the written case summary prepared by the learner. The premise of investigating the case summaries is that it captures the thought and process of the learners in solving the cases; since, the case summaries hold important reasoning information, it makes sense to incorporate it as part of the novice-expert overlay model. In this paper, case summaries written by novices and experts were considered as an addendum to the existing novice-expert overlay model in the BioWorld system. Toward this goal, using a promising new classification method called confidence-weighted linear classifiers, this paper proposes a way to augment the novice-expert overlay model in BioWorld.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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