Validating the National Survey of Student Engagement (NSSE) at a Research-Intensive University
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
The National Survey of Student Engagement (NSSE) has been used at universities across the U.S. and Canada to gather information about the quality of engagement of first-year students and graduating students. Institutions use NSSE’s five benchmarks of effective educational practice to compare themselves with other schools and to focus in on ways to improve the educational experiences of their students. However, studies indicate that these benchmarks may not be a valid way to convey NSSE information. This study was conducted to investigate whether or not NSSE’s five-factor model is the best fit for student engagement data collected at a large, public, research-intensive, land-grant university. The five-factor model did not fit the data for the 2008 sample of senior students at this university. Rather, a revised model using six factors instead of five and 21 of 42 items provided a more valid test blueprint. This new model was then tested and found to fit the 2011 sample of senior students at the same university. Discussion regarding use of a nationally collected data at an individual institution is provided.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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