Innovation in Learning-Oriented Language Assessment (Book Review)
Bibliographic record
Abstract
Language assessments can have a wide range of purposes and uses, ranging from high-stakes university entrance exams to classroom-based assessments used to monitor and provide feedback to learners. This last grouping of assessments, often referred to as formative or learning-oriented assessment, not only provides teachers important information about students’ progress, but it also provides learners a platform to learn and grow as a result of the assessment process itself (Turner & Purpura, 2016). There has been a wide range of literature published on formative and learning-oriented assessment over the past two decades, including book-length treatments (Gebril, 2021), but little has been written on the practical approaches in relation to teacher education, particularly in adapting assessment practices to keep pace with evolving technology. This is important to address due to changes in classroom formats, with online, asynchronous, hybrid, and flipped classes becoming more common. Sin Wang Chong and Hayo Reinders address this gap in their recent edited volume Innovation in Learning-Oriented Language Assessment. This book explores recent trends in this approach by discussing assessment practices from a wide range of international teachers and researchers from Brazil, Canada, China, Hong Kong, Japan, Norway, Saudi Arabia, Spain, Turkey, and the UK. [First Paragraph]
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".