The conceptualization and development of a high-stakes video listening test within an AUA framework in a military context
Bibliographic record
Abstract
The concepts of justification and accountability are being promoted as a new added value in the field of language testing. Bachman and Palmer's (2010) Assessment Use Argument (AUA) provides a theoretical framework that can ensure the validity of a test. This study implements an AUA in a high-stakes context to justify the inclusion of videos in a general proficiency listening comprehension test intended for international military personnel studying English in Canada. It follows a three-phase exploratory sequential mixed methods research design. The first phase includes a needs analysis and the development of a prototype. Qualitative data was collected that provided a basis for the next phase. Phase Two includes the development of a computer-delivered video listening test, which follows each stage of test development. In the final phase, Phase 3, the test was trialled on three groups of stakeholders (test developers, teachers and students) and their perceptions of the usefulness of the videos were collected through both qualitative and quantitative methods. The results show that stakeholders perceived the videos as being helpful for comprehension and they appreciated the authenticity of the listening texts. The stakeholders also reported that the videos reduced student anxiety. These data were used as evidence for Claim 1 of the AUA, which states that the use of a test must produce beneficial consequences for the test taker. Though the present study focuses on Claim 1, it does clearly articulate the other three claims of the AUA, which refer to the Decisions, Interpretations, and Assessment Records, as explained by Bachman & Palmer (2010).
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| 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".