Developing Literacy Skills through Collaborative Tasks for Emerging-Proficiency English as Additional Language Learners in Quebec
Bibliographic record
Abstract
Literacy in a first language or in additional languages involves a set of complex cognitive, social, and linguistic skills that develop over time. However, pedagogical materials for low-proficiency English as an additional language (EAL) learners tend to target low-level literacy skills only, such as responding to fact-based questions. Materials that target the development of high-level literacy skills, such as integrating information and reasoning based on inferencing, with these learners are rare, despite these skills being necessary in today’s multimodal, technological world. In this article, we argue there is a need to develop high-level literacy skills with low-proficiency L2 learners which, ultimately, are not language-specific. Drawing on theories of additional language learning and task-based language teaching, we created a multi-day literacy task for low-proficiency learners consisting of various activities. For each activity, we provide examples of the materials with which the students worked and examples of authentic student work, which we obtained through a piloting phase. La littératie dans une langue première ou additionnelle implique un ensemble de compétences cognitives, sociales et linguistiques complexes qui se développent au fil du temps. Cependant, le matériel pédagogique dédié aux apprenants de l’anglais comme langue additionnelle ayant un niveau bas de compétences tend à cibler uniquement les compétences de littératie de bas niveau, telles que les réponses à des questions factuelles. Le matériel qui vise le développement, chez cette catégorie d’apprenants, des compétences de littératie de niveau élevé, telles que l’intégration de l’information sur la base de l’inférence, est rare malgré la nécessité de ces compétences dans le monde multimodal et technologique d’aujourd’hui. Dans cet article, nous soutenons qu’il y’a besoin de développer des compétences en littératie de haut niveau auprès des apprenants de basniveau de langues secondes, des compétences qui ne sont ultimement pas spécifique à une langue. En nous basant sur les théories d’apprentissage des langues additionnelles et l’enseignement des langues basé sur les tâches, nous avons élaboré une tâche de littératie étalée sur plusieurs jours et formée de plusieurs activités, s’adressant aux apprenants d’un niveau bas de compétences. Pour chaque activité, nous présentons des exemples du matériel mis à la disposition des apprenants ainsiqu’un échantillon de travaux authentiques d’apprenants ayant réalisé les activités durant une phase pilote.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.167 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".