TBL and Teacher Preparation: Toward a Curriculum for Pre-service Teachers
Bibliographic record
Abstract
English language learners (ELLs) represent a growing demographic in the elementary mainstream classroom of today. Initial teacher education (ITE) programs must prepare teacher candidates for the dual challenge of teaching curriculum content while supporting the development of English language proficiency. Task-based learning (TBL) holds potential for addressing these learner needs. This article describes the curriculum and provides a list of suitable readings and tasks (assignments) for a 3-hour (one full course-equivalent) university course at the pre-service level that bridges theory to practice, and prepares elementary-route teachers to design and implement TBL in the context of the mainstream class setting. By basing the proposed curriculum on TBL, a model is provided for students to learn firsthand how TBL may be implemented in the mainstream.Les apprenants de l’anglais constituent un groupe démographique croissant dans les salles de classe au primaire. Les programmes de formation initiaux des enseignants doivent préparer les stagiaires pour le double défi que représentent l’enseignement du contenu et le développement de la compétence en anglais chez les élèves. L’enseignement basé sur les tâches (EBT) est susceptible de répondre à ces besoins. Cet article décrit un programme d’étude et offre une liste de lectures et de tâches appropriées pour un cours universitaire complet du premier cycle qui lie la théorie à la pratique et prépare les étudiants à concevoir et mettre en pratique l’EBT dans les classes au primaire. Le programme d’étude proposé fournit donc aux étudiants en pédagogie un modèle de la mise en œuvre de l’EBT en salle de classe.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".