Guiding Students’ Transition to University: Which Student Factors to Include?
Bibliographic record
Abstract
In Belgium, pupils in their final years of high school follow an orientation trajectory towards higher education, including self-exploration tests and participation in initiatives of higher education institutions, under the supervision of their teachers. At the end of this trajectory, the teacher board advises pupils regarding their intended study choices. Belgian higher education has an open-admission system, although there exist non-binding positioning tests for some of the Bachelor degree programmes, developed at the request of the Flemish government. The aim of this study is to develop a new orientation tool to support teachers and the teacher board in their guiding role for high-school students transitioning to higher education. In cooperation with 43 high schools, important factors to be included in the instrument were investigated. Student factors rated by the teacher board such as test taking and preparation strategies, persistence and effort and factors regarding prior education were examined as predictors of students’ academic performance at two higher education institutions (n = 2852). Based on this research, a prototype of a new orientation instrument is presented that takes into account high school GPA and the match between students’ field of study in secondary school and their intended/chosen Bachelor programme in higher education. These factors have a high multiple correlation of approximately nearly 0.70 with academic performance at university. The other student factors considered are substantially related to study success in higher education, but appear largely incorporated into student’s high school GPA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".