Application of Network Analysis to Description and Prediction of Assessment Outcomes
Bibliographic record
Abstract
With the use of computerized testing, ordinary assessments can capture both answer accuracy and answer response time. For the Canadian Programme for the International Assessment of Adult Competencies (PIAAC) numeracy and literacy subtests, person ability, person speed, question difficulty, question time intensity, fluency (rate), person fluency (skill), question fluency (load), pace (rank of response time within question), and person pace were assessed. Undirected Gaussian Graphical Model networks of the measures based on partial correlations were predictive of the measures as nodes. The population-based model extrapolated well to individual person estimations. Finally, it is shown that the “training” Canadian model generalized with minor differences to four other English-speaking PIAAC assessments (USA, Great Britain, Ireland, and New Zealand). Thus, the undirected network approach provides a heuristic that is both descriptive and predictive. However, the model is not causal and can be taken as an example of “mutualism”.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".