Raising Awareness about Task Assessment Rubrics in Task Based Language Teaching
Bibliographic record
Abstract
Researchers have examined the benefits of employing a complex set of assessment rubrics as a framework for course development, teaching, learning and assessments in language programs. However, no research has explored ways to mitigate challenges faced by adult international and immigrant second language learners new to learner-centered and rubric guided curriculum that requires critical thinking and self-regulation. To raise awareness about writing task assessment rubric criteria, this qualitative study through iterative cycles of practitioner action research used Community of Inquiry (CoI) as a framework and a writing task assessment rubric as a hook to facilitate asynchronous written peer feedback in task-based English as Additional Language (EAL) learning environment. During the seven-week long intensive language course at a post-secondary institute in Western Canada, 20 adult multi-lingual participants from didactic learning environments completed 28 tasks in the class and engaged in providing peer feedback using an institutionally mandated Canadian Language Benchmark (CLB) rubric in nine asynchronous forums. Sources of evidence from asynchronous feedback transcript, writing tasks completed by learners, and the instructor’s observation notes and journals was analyzed for themes using NVIVO regarding cognitive presence, teaching presence and social presence elements in CoI framework. Results showed: (1) learners had low level of rubric awareness at the beginning of the course (2) intervention facilitated scaffolding (3) learners re-conceptualized writing and the role of rubric, and (4) asynchronous peer feedback increased rubric awareness and competency in writing. Implications are discussed in relation to adult international and immigrant language learners, task-based language teaching, and the interface between automatized explicit and implicit knowledge of rubric criteria in rubric based second language curriculum.
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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 | 0.054 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".