Implementing Portfolio-Based Language Assessment in LINC Programs: Benefits and Challenges
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
Although earlier research has examined the potential of portfolios as assessment tools, research on the use of portfolios in the context of second-language education in Canada has been limited. The goal of this study was to explore the benefits and challenges of implementing a portfolio-based language assessment (PBLA) model in Language Instruction for Newcomers to Canada (LINC) programs. Data were gathered through semistructured interviews with four LINC instructors involved in a PBLA pilot project in a large Canadian city. Similar interviews were con- ducted with a representative of Citizenship and Immigration Canada, and a de- veloper of the PBLA model. Participants identified both benefits and challenges related to PBLA implementation. Based on their feedback, recommendations for future implementation are provided.Bien que la recherche antérieure ait porté sur le potentiel des portfolios comme outils d’évaluation, la recherche sur leur emploi dans l’éducation en langue sec- onde au Canada est limitée. L’objectif de cette étude est d’explorer les bienfaits et les défis relatifs à la mise en œuvre d’un modèle d’évaluation linguistique reposant sur le portfolio (PBLA) pour la formation dans les cours de langue pour immi- grants au Canada (CLIC). Les données ont été recueillies par le biais d’entrevues semi-structurées avec quatre enseignants de CLIC impliqués dans un projet pilote PBLA dans une grande ville canadienne. Des entrevues similaires ont eu lieu auprès d’un représentant de Citoyenneté et immigration Canada et d’un développeur du modèle PBLA. Les participants ont identifié les bienfaits et les défis relatifs à la mise en œuvre du modèle PBLA. En s’appuyant sur leur rétroac- tion, on fournit des recommandations visant la mise en œuvre à l’avenir.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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