Implementing Portfolio-Based Language Assessment in LINC Programs: Benefits and Challenges
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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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".