Adaptive Test Design with a Naive Bayes Framework.
Bibliographic record
Abstract
Abstract. Bayesian graphical models are commonly used to build student models from data. A number of standard algorithms are available to train Bayesian models from student skills assessment data. These models can assess student knowledge and skills from a few observations. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the information they will provide. In practice, such data often contains missing values and, under some circumstances, missing values far outnumber observed values. However, when collecting data from test results, one can often choose which values will be present or missing by a consequent test design. We study how to optimize the choice of test items for collecting the data that will be used for training a Bayesian CAT model, such as to maximize the predictive performance of the model. We explore the use of a simple heuristic for test item choice based on the level of uncertainty. The uncertainty of an item is derived from its initial probability of success and, thus, from its difficulty. The results show that this choice does affect model performance and that the heuristic can lead to better performance. Although the study’s results are more exploratory than conclusive, they suggest interesting research avenues. 1
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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 | 0.059 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".