UDL International Symposium:Learning Together
Bibliographic record
Abstract
With the implementation of Languages Connect, Ireland’s Strategy for Foreign Languages in Education, four new foreign languages have been introduced into the Leaving Certificate curriculum, namely Polish, Lithuanian, Portuguese and Mandarin Chinese. The pitches of the curriculum specifications vary from pre-A1/A1 to A2/B1 as in the Common European Framework of Reference for Languages, while the courses are open to any student regardless of language background and proficiency. It is a norm for the new language classrooms to have both heritage and non-heritage students learning together. Therefore, Universal Design for Learning (UDL) was identified by teachers of the new curricular languages to be one of the main and consistent challenges for teaching and learning as well as for curriculum implementation. On the other hand, the provision of new languages has also shed light on the understanding of Additional Educational Needs (AEN). It is reported particularly by teachers of Mandarin Chinese that students with diagnosed dyslexia do not necessarily perform inadequately in Mandarin courses. On the contrary, in a number of cases, these students outperform other students in the perception and production of Mandarin scripts. It is time to review the understanding of AEN and the perception of its impact on language learning with a broader view of the language landscape and in the context of UDL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.255 | 0.113 |
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".