Process and Product in Computer-Based Assessments
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it