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]
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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 it