Developing a user-oriented second language comprehensibility scale for English-medium universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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(target end-users) rating international university students’ speech samples drawn from Isaacs and Trofimovich’s (2011) unpublished corpus, the instrument was expanded to a 6-level scale through iterative revisions.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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it