Some drivers of test item difficulty in mathematics : an analysis of the competency rubric
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
This paper is concerned with the empirical validation of the competency rubric described in another paper presented at the same conference: Turner, Ross (April 2012). Some drivers of test item difficulty in mathematics. Paper presented at the Annual Meeting of the American Educational Research Association (AERA), Vancouver, 13-17 April 2012 http://research.acer.edu.au/pisa/4/ Using items developed for the PISA 2012 survey, and data collected as part of an extensive field trial of the PISA tasks conducted during 2011 in some 67 countries, the authors use multidimensional Rasch modelling and latent regression to examine the following three questions: 1. What is the level of agreement among raters when they apply the competency rubric? 2. Does each of the competencies capture different dimensions of cognitive complexity in the tasks? 3. To what extent do ratings of the cognitive complexity account for (predict) the difficulty of the tasks for students?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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