The Student Retention Puzzle Revisited: The Role of Institutional Image
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
This study aims to develop and test a student retention model that includes system and institutional dropout as outcome variables, examining differences in factors that affect them. We also model the image of the institution as influencing institutional commitment and drop/stay intentions. Using structural equation modeling to test the hypotheses, we found that both initial personal and institutional characteristics (such as students' goal commitment and the higher education institutional image), as well as the institutional experience and integration of the student into the academic environment, will have an effect on the level of student performance and institutional commitment, which in turn influence stay/drop decisions. Higher education administrators need to manage not only conventional factors—such as instructional effectiveness, peer interaction, and academic integration—in order to reduce attrition. They also need to manage brand associations with regard to the positioning of their institution in prospects' minds.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it