Process and Product in Computer-Based Assessments
Bibliographic record
Abstract
Abstract: There is no consensus among assessment researchers about many of the central problems of response process data, including what is it and what is it comprised of. The Standards for Educational and Psychological Testing ( American Educational Research Association et al., 2014 ) locate process data within their five sources of validity evidence. However, we rarely see a conceptualization of response processes; rather, the focus is on the techniques and methods of assembling response process indices or statistical models. The method often overrides clear definitions, and, as a field, we may therefore conflate method and methodology – much like we have conflated validity and validation ( Zumbo, 2007 ). In this paper, we aim to clear the conceptual ground to explore the scope of a holistic framework for the validation of process and product. We review prominent conceptualizations of response processes and their sources and explore some fundamental questions: Should we make a theoretical and practical distinction between response processes and response data? To what extent do the uses of process data reflect the principles of deliberate, educational, and psychological measurement? To answer these questions, we consider the case of item response times and the potential for variation associated with disability and neurodiversity.
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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 | 0.081 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".