Developing a user-oriented second language comprehensibility scale for English-medium universities
Bibliographic record
Abstract
There is growing research on the linguistic features that most contribute to making second language (L2) speech easy or difficult to understand. Comprehensibility, which is usually captured through listener judgments, is increasingly viewed as integral to the L2 speaking construct. However, there are shortcomings in how this construct is operationalized in L2 speaking proficiency scales. Moreover, teachers and learners have little practical means of benefiting from research pinpointing the properties of learners’ oral performance that optimize or hinder their ability to be understood. There is thus the need for a tool to guide teachers on what to focus on in instruction in order to target more effectively the linguistic factors that matter most for being understood and to raise learners’ awareness about their abilities. To address this gap, this article reports on the development of an L2 English comprehensibility scale targeting the degree of perceived listener effort required for understanding L2 speech. The starting point was Isaacs and Trofimovich’s (2012) preliminary 3-level empirically based L2 English comprehensibility scale, restricted for use with learners from one first language (L1) background on a single task. Through focus group consultations and piloting involving nine Canada- and UK-based English for Academic Purposes teachers (target end-users) rating international university students’ speech samples drawn from Isaacs and Trofimovich’s (2011) unpublished corpus, the instrument was expanded to a 6-level scale through iterative revisions. The resulting formative assessment tool is intended for use with pre- and in-sessional university students from mixed L1 backgrounds on academic extemporaneous speaking tasks to support their oral language development.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".