ESL/EFL instructors’ classroom assessment practices: purposes, methods, and procedures
Bibliographic record
Abstract
Student assessment plays a central and important role in teaching and learning. Teachers devote a large part of their preparation time to creating instruments and observation procedures, marking, recording, and synthesizing results in informal and formal reports in their daily teaching. A number of studies of the assessment practices used by teachers in regular school classrooms have been undertaken (e.g., Rogers, 1991; Wilson, 1998; 2000). In contrast, less is known about the assessment practices employed by instructors of English as a Second Language (ESL) and English as a Foreign Language (EFL), particularly at the tertiary level. This article reports a comparative survey conducted in ESL=EFL contexts represented by Canadian ESL, Hong Kong ESL=EFL, and Chinese EFL in which 267 ESL or EFL instructors participated, and documents the purposes, methods, and procedures of assessment in these three contexts. The findings demonstrate the complex and multifaceted roles that assessment plays in different teaching and learning settings. They also provide insights about the nature of assessment practices in relation to the ESL=EFL classroom teaching and learning at the tertiary level.
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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 | 0.044 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".