Developing a Badge System for a Community ESL Class Based on the Canadian Language Benchmarks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teaching multilingual, multilevel language classes presents many challenges including helping students learn level-appropriate language. This learner-centred approach is complicated in a multilevel classroom where the teacher cannot always focus on each student’s needs. As a result, learner motivation and attendance are frequent problems. This article details the adaptation of the Canadian Language Benchmarks (CLB) into a badge system to help learners in a community English as a Second Language (ESL) program set and track personalized language learning goals. The authors explain the purposes of badges, including motivation and assessment, and describe how to create a CLB-based badge system for curriculum and assessment purposes. The authors also share feedback from students and instructors in the community ESL program about the badge-based curriculum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 it