Academic Culture: Its Meaning, Measure and Contribution to Student Learning
Bibliographic record
Abstract
This study had two objectives. One objective was to assess the psychometric properties of a survey instrument measuring a new latent variable, Academic Culture (AC), combining three observed variables academic press, disciplinary climate and teachers’ uses of instructional time. The second objective was to replicate the results of an earlier study identifying AC as a significant mediator of school leadership’s influence on student learning. Data for the study were provided from 2068 teachers located in 49 schools in 14 Texas school districts, as well as student achievement data from the State of Texas Assessments of Academic Readiness (STAAR) and student socioeconomic (SES) data available from school websites. Second order Confirmatory Factor Analysis (CFA) and Many-Facet Rasch (MFR) models were used to examine the survey instrument’s construct validity and its measurement invariance. Structural Equation Modeling was used to identify the extent to which AC mediated the effects of school leadership on student achievement controlling for student SES. Rasch analysis and CFA confirmed the measurement invariance and several forms of validity of the survey instrument. Replicating the results of an earlier study, results of structural equation modeling demonstrated significant effects of AC on student achievement and identified AC as a significant mediator of school leadership effects on student achievement. The study contributes to the quality of instruments available to school leaders for their school improvement work and to researchers inquiring about the most promising variables mediating the indirect effects of school leadership on student success.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".