Investigating Conditions for Student Success at an American University in the Middle East
Bibliographic record
Abstract
It is of great significance to an institution of higher education to meet its goals and to establish its institutional effectiveness and that it has a framework for discussing its institutional performance results, accordingly this study aims to investigate a) the conditions for student success at the University with respect to the five benchmarks of effective educational practices (Kuh, 2009); b) significant differences in conditions for student success across important student populations (gender, GPA, number of credits completed, and academic year); and c) how do these conditions contribute to outcomes valued by the institution (students’ growth, satisfaction, and recommendation of the University). Responses of 1853 students’ on the College Outcomes Survey (COS) for years 2007-2010 were used to answer the various research questions of the study. COS Items were selected that measured students’ time allocations and engagement in various activities reflecting effective educational practices, in addition to valued outcomes. Data analysis involved first testing the measurement model and estimating overall fit of the data using confirmatory factor analysis (CFA). Descriptive statistics, and correlations were reported for the benchmarks of effective educational practices, and differences in benchmark experiences by subgroup were investigated. Finally, a structural model tested the influence of benchmarks of academic practice on valued outcomes and a regression was conducted to investigate relationship between student activities and the benchmarks. Results revealed good fit of the data for the model, identified University’s performance on benchmarks of effective educational practices and their relationship to outcomes valued by the University. Implications for practice were discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".