The Transfer Student Transition: Factors Impacting the Experiences of Undergraduate Students Upon Transfer
Bibliographic record
Abstract
Previous research has shown that the experiences of students that transfer from other two-year or fouryear institutions are different from students coming directly from high school. With the difference in experiences comes a demand for a variety of resources and availability of tools specific to transfer students. At Iowa State University (ISU) nearly a quarter of the undergraduate students are transfer students, making the transfer student experience especially important. Furthermore, transfer student retention rate at ISU was 3.79% lower than for directfrom- high-school students in 2017. Lower retention rate was the driving factor for the development of a survey instrument that would help us better understand the transfer experience. Themes collected from focus groups in 2017 provided the topics for this survey instrument. As a result of the survey instrument, factors that affect the transition of transfer students in the Animal Science Department, were identified. Data collected from this study and future related studies will be used to inform policy and procedures related to the transfer student transition at the departmental, college, and university level.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".