Building Teachers’ Assessment Capacity for Supporting English Language Learners Through the Implementation of the STEP Language Assessment in Ontario K-12 Schools
Bibliographic record
Abstract
The Ontario Ministry of Education recently implemented the Steps to English Proficiency (STEP) language assessment framework to build educator capacity for addressing the needs of English language learners (ELLs) in K-12 schools. The STEP framework is a set of descriptors-based language pro ciency scales that specify observable linguistic behaviours from which educators can make inferences about students’ English language development. Teachers use these proficiency scales to assess, document, and track students’ language pro ciency development based on daily interactions with students in classrooms. The purpose of this article is to report on teachers’ perceptions of and experiences with the STEP proficiency scales during a three-year pilot implementation and validation study of the initiative. Based on analysis of these ndings, we articulate implications for building teachers’ assessment capacity using observational language assessment scales for K-12 ELLs. Le Ministère de l’Éducation de l’Ontario a récemment mis sur pied un cadre d’évaluation des compétences linguistiques (Steps to English Proficiency - STEP) pour accroitre la capacité des enseignants à répondre aux besoins des apprenants d’anglais dans les écoles K-12. Le cadre STEP est un ensemble d’échelles de compétences linguistiques basées sur des descripteurs qui décrivent des comportements linguistiques à partir desquels les enseignants peuvent faire des inférences quant au développement des élèves en anglais. Les enseignants se servent de ces échelles de compétence pour évaluer, documenter et suivre le déve- loppement langagier de leurs élèves dans leurs interactions quotidiennes avec les élèves en classe. L’objectif de cet article est de faire état des perceptions et des expériences des enseignants relatives aux échelles de compétences STEP pendant les trois années de la phase de mise en oeuvre initiale et d’étude de la validité de l’initiative. Nous nous appuyons sur les résultats de notre analyse pour formu- ler des implications relatives à l’accroissement de la capacité des enseignants par l’emploi des échelles de compétences linguistiques auprès d’apprenants d’anglais en K-12.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".