On the Faithfulness of Simulated Student Performance Data.
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
Abstract. The validation of models for skills assessment is often conducted by using simulated students because their skills mastery can be predefined. Student performance data is generated according to the predefined skills and models are trained over this data. The accuracy of model skill predictions can thereafter be verified by comparing the predefined skills with the predicted ones. We investigate the faithfulness of different methods for generating simulated data by comparing the predictive performance of a Bayesian student model over real vs. simulated data for which the parameters are set to reflect those of the real data as closely as possible. A similar performance suggests that the simulated data is more faithful to the real data than for a dissimilar performace. The results of our simulations show that the latent trait model (IRT) is a relatively good candidate to simulate student performance data, and that simple methods that solely replicate mean and standard deviation distributions can fail drastically to reflect the characteristics of real data. 1
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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 | 0.001 | 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 | 0.004 | 0.001 |
| 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