The battle for Latin in UK universities: a statistical analysis of factors driving student success and failure in beginners’ Latin modules
Bibliographic record
Abstract
In the UK, Latin is often seen as an elitist subject taught largely at fee-paying schools. Over the past generation, however, great strides have been made in opening up the subject to students from all backgrounds. A major hindrance to widening access to Latin at university level is that the language can often prove challenging for students. Data collected for this article reveal that only 77% of Latin students on beginners’ modules in UK universities achieved a pass. Or in other words, nearly a quarter of students embarking on the study of Latin either fail or withdraw from their module.This article seeks to investigate the problems of retention and progression in support of the battle to make the study of Latin sustainable and accessible in higher education. By analysing survey responses from 29 UK universities offering beginners’ Latin modules, it explores the impact of factors such as module weighting and duration, contact hours, class sizes, textbooks and assessment methods. In so doing, it breaks new ground in its rigorous statistical analysis of a significant set of quantitative data in an effort to improve our understanding of successful ancient language teaching, tackle real-world issues of retention, and promote student success.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".