‘Experience Congruence’ as a Criterion for Generalizability?
Bibliographic record
Abstract
When evaluating the applicability of published SoTL and/or educational research results faculty often focus on the differences in demographic characteristics between the students in their academic context and those of the students where the data were collected. This could be problematic since readers might choose to dismiss a particular innovation if they perceive the discrepancies to be significant even though this reliance on demographics to identify informative pedagogical research may not always be justified. We report the results of survey of 1326 students from three introductory-level, first-year chemistry courses (a total of ten sections with ten different instructors) at two universities with significantly different student populations. The survey asked students to choose the hardest and easiest from five groups of topics typically taught in first-year chemistry courses. Remarkably, when separated by lecture section, overlaid frequency plots of students’ choices of hardest topic revealed a singular pattern. The trend transcended universities, courses, textbooks, instructors, and demographics. The only common parameter between the samples was the chemistry topics they learned. The correspondence in content, as such, constituted an “experience congruence”. Based on these data, we propose that readers might consider experience congruence – in lieu of sample or population characteristics – as a criterion for judging the generalizability of educational data.
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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 | 0.513 | 0.736 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".