‘Experience Congruence’ as a Criterion for Generalizability?
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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