Does Portfolio-Based Language Assessment Align with Learning-Oriented Assessment? Evidence from Literacy Learners and their Instructors
Bibliographic record
Abstract
A high-stakes Portfolio-Based Language Assessment (PBLA) protocol that was fully implemented in all Language Instruction for Newcomers to Canada (LINC) programs in 2019 requires instructors and students to set language-learning goals and complete, compile, and reflect on numerous authentic language tasks. Due to the language barriers incurred when communicating with beginner English-as-a-second-language literacy learners (BELLs), no PBLA research has been conducted with BELLs. To address this gap, we interviewed 26 BELLs (n = 2 from 13 L1s) and their instructors (n = 4) about their understanding and use of PBLA. Student interviews were conducted with the assistance of bilingual interpreters in the students’ L1s. All the interviews were then transcribed and thematically analyzed in relation to PBLA’s alignment with the six dimensions in Turner and Purpura’s (2016) learning-oriented assessment framework: contextual, elicitation, proficiency, learning, instructional, interactional, and affective. Results have implications for optimizing learning, and task-based instruction and assessment practices in LINC.
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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 | 0.092 | 0.283 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".