Pragmatic Measurement for Education Science: A Method-Substance Synergy of Validation and Motivation
Bibliographic record
Abstract
Education researchers often require quick and efficient assessments of various student characteristics (e.g., motivation) to use in classroom settings.Unfortunately, guidelines for addressing practical measurement obstacles, such as scale length, are ambiguous at best and non-existent at worst.Many measures lack sufficient evidence that the conclusions they produce are merited, and short measures have received particular criticism from measurement experts.The result is a tension between technical and pragmatic constraints when conducting measurement in field research.This three-paper dissertation is aimed at identifying and addressing these tensions in one area of motivation research.Paper 1 provides the substantive frame for the overall dissertation.The goal was to understand short-term student motivation change in a classroom setting.Paper 2 provides a typical approach to assessing a scale's quality and viability for use in the field.The goal was to use traditional psychometric approaches to evaluate a brief measure of motivation.Finally, Paper 3 presents a pragmatic approach to determining validity evidence (i.e., pragmatic measurement) by considering the underlying uses and restrictions of collecting data.The goal was to evaluate the pragmatic approach as a framework for measure users to identify the relevant validity evidence needed based on the potential uses and interpretations of a measure.Together, these papers highlight the nature and benefit of advancing methodological goals by pursuing substantive goals.The current research is a methodological-substantive synergy (i.e., work that advances a substantive domain, such as motivation, while developing and utilizing state-of-the-art methodology) aimed alleviating technical and practical tensions.
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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 | 0.321 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".