Development of a Spanish generic writing skills scale for the Colombian Graduate Skills Assessment (Saber Pro)
Bibliographic record
Abstract
While many higher education institutions list the generic skills their graduates are intended to acquire during a course of study (Barrie, 2006), the relevant skills are rarely directly assessed at graduation. In Colombia, exit assessment is compulsory for all post-secondary training and education. To this end, a Spanish-language version of the Australian Graduate Skills Assessment (GSA) was developed for the Colombian context. However, problems were identified with the reliability of the Spanish version of the GSA writing scale. This paper describes the process of replacing the original version of the Spanish-language version of the GSA scale (an intuitively based writing scale) with an empirically based scale developed using a question tree method. Forty raters constructed two holistic (combined trait) and three analytic (individual trait) writing scales using benchmarked scripts from a previous test administration. The five scales were then trialled. Comparison of the scales showed the eight-level holistic scale provided the widest distribution of scores. This research provides insights into generic writing skill testing for higher education graduates in Colombia. In addition, the study uniquely provides a detailed description of the development of empirically-based analytic and holistic scales for assessing the writing of Spanish-L1 speaking graduates in Colombia.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".