Using Potential Performance Theory to Assess How to Increase Student Consistency in Taking Exams
Bibliographic record
Abstract
It has been a concern among educators and academics that U.S. students suffer from a lack of knowledge about the world around them. This is reflected in low history scores, particularly in world history. The common explanation for this is that there is some systematic deficiency in American students, in that they either do not know the material or have poor testing strategies. We offer a different way of looking at this problem using Potential Performance Theory (PPT). With PPT, we assessed the consistency with which students answered test questions and show how much performance would improve if a student were perfectly consistent. Furthermore, we show how much improvement there is in consistency over multiple sessions. Participants were given a short world history test six times in a row. The results were interesting. Consistency did improve with practice, but the systematic factors that students employed (e.g. strategies) were poor enough to counter-act the improvement due to rising consistency levels.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".