A Comparison of Three Retrospective Self-reporting Methods of Measuring Change in Instructional Practice
Bibliographic record
Abstract
In the post + retrospective pretest method of measuring change, evaluators ask the respondents to recall pre-intervention status at posttest time. Research has produced strong evidence in support of this approach over the pretest-posttest approach to measuring change. However, no research has yet to examine and compare different forms of retrospective methods. We compared three retrospective methods of measuring elementary grade teachers’ self-reported change in mathematics instructional practices: the post + retrospective pretest method (reporting current practices and earlier practices), the post + perceived change method (reporting current practice and the amount and direction of change), and the perceived change method (reporting only the amount and direction of change). Teachers in the post + retrospective pretest condition reported least change, followed by teachers in the post + perceived change condition; teachers in the perceived change condition reported the greatest change. We can explain our findings in terms of differential satisficing (the tendency to exert minimal effort in responding) caused by differences in cognitive demands among the three methods. Greater task difficulty leads to greater satisficing, which causes respondents to resort more to socially desirable responses. A greater tendency to provide socially desirable responses leads to relying on expected implicit theory of change and subsequently reporting greater change in instructional practices.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".