Teaching and assessing language skills: Defining the knowledge that matters
Bibliographic record
Abstract
A goal of this double issue of English Teaching: Practice and Critique is to collectively consider what we mean when we talk about knowledge about language. How have our understandings changed over time? What are the implications of these new understandings for pedagogy in the field of language teaching? These are necessary and important questions. This article, however, does not attempt to address them. Rather, it focuses on the power of standardized assessment in language education and on its implications for the discussions contained within this journal. Central to this paper is the argument that standardized language assessments are resistant to change, rarely integrating new understandings of language into assessment designs. This reticence in turn limits advances in pedagogy. Language theorists and educators are therefore compelled to advocate for assessment reform. Drawing on a study of government-mandated writing assessment and its impact on Grade 12 academic students in Alberta, Canada, this article demonstrates how poorly developed standardized assessments curtail teaching and learning. The article concludes with a discussion of validity theory and its implications for test design, demonstrating how validity research can be used to ensure that standardized language tests value and support new understandings of language theory. Whose knowledge about language counts most in the Language and Literacy classroom? a. the teacher's b. the students' c. the language and literacy researcher's d. the high-stakes assessment designers' e. the curriculum developer's f. the cognitive psychologist's g. none of the above h. all of the above i. some of the above
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.141 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.009 | 0.098 |
| Scholarly communication | 0.039 | 0.059 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.015 | 0.018 |
| 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".