DOES VIDEO BELONG IN L2 ACADEMIC LISTENING TESTS? STAKEHOLDERS’ PERCEPTIONS ABOUT TEST DIFFICULTY, AUTHENTICITY, AND MOTIVATION
Bibliographic record
Abstract
Whether visual information belongs in second language (L2) listening tests has long been a subject for scholarly debate, with L2 learners’ performance on and perceptions of video-based tests being the primary sources of evidence. The research into L2 teachers’ perceptions, however, is scarce, as is the research into stakeholders’ views of content visuals, such as a graph or diagram, in a listening assessment construct. This study sought to bridge these gaps by exploring stakeholders’ perceptions of audio-only vs. video-based academic listening tests, the latter featuring a combination of context (e.g. the speaker’s posture) and content (e.g. a graph) visuals. The questionnaire data from 143 English-as-a-second-or-foreign language (ESL/EFL) learners and 310 ESL/EFL teachers showed that both the learners and the teachers generally found video lectures to be less difficult, more motivating, more authentic, and suitable for high-stakes L2 academic listening tests. Although most stakeholders favored video-based lectures, wide variation among the learners’ and teachers’ perceptions suggests that some stakeholders had reservations about the role of videos in listening tests. These findings are discussed in the light of their implications for the assessment construct of L2 academic listening.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".