Examining the Transfer Student Experience: Interactions with Faculty, Campus Relationships, & Overall Satisfaction
Bibliographic record
Abstract
Paper presented at the annual meeting of the Association for the Study of Higher Education in Vancouver, Canada, November 2009Transfer students make up a substantial share of undergraduates at four-year institutions in the United States. Among 1999-2000 bachelor’s degree recipients, about one in three reported that they had transferred to their degree-granting institution (Peter & Cataldi, 2005). In a nationally representative sample of undergraduates in 2003-04, half of fourthand fifth-year students at four-year institutions reported that they began their postsecondary education at a different institution (U.S. Department of Education, 2009). In view of the substantial share of undergraduates at baccalaureate-granting institutions who transfer, 1 it is important to assess the educational experience of these students, who are likely to face academic, social, and personal challenges in the transition to a new institution (Ishitanti, 2008; Laanan, 2001; Townsend & Wilson, 2006). Students may change institutions for a number of reasons. For bachelor’s degree seeking students transferring from a sub-baccalaureate institution (vertical transfers), transfer is a necessary step to reaching their educational objective. The motives behind horizontal transfer, by contrast, are far more varied, including unsatisfactory academic performance,
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".