Neue Methoden zur Eignungsberatung an Hochschulen – Eine experimentelle Analyse eines webbasierten Self-Assessments [New Methods to Advise Students - A Field Experimental Approach to Test an Online Self-Assessment]
Bibliographic record
Abstract
Depending on the field of major, between a quarter and half of German students in higher education quit prematurely. As a means to reduce the drop-out rate many universities have launched online self-assessments to provide guidance when applicants choose among different subjects. However, so far very little is known about how self-assessments impact the applicants’ choice. We use the pilot-phase of a self-assessment to conduct a field experiment which allows us to analyse students’ behaviour. Our results show that self-assessments causally influence the enrolment decisions of the candidates. Good grades in lower education and general qualifications for university entrance (as opposed to lower entrance degrees) increase the probability to attend (voluntary) self-assessments. Furthermore, assessments may change the expectations of participants with respect to their future academic success. However, so far the probability to participate in the self-assessment is relatively low.
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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 | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".