Investigating the Validity of a University-Level ESL Speaking Placement Test via Mixed Methods Research
Bibliographic record
Abstract
The current study investigated the validity of a locally-developed university-level English as a Second Language (ESL) speaking placement test using a mixed-methods design. We adapted Messick’s integrative view of validity (1996) and Kane’s interpretation argument framework (2013) and focused on two sources of validity evidence: relations to other variables, and consequences of testing (AERA, APA, and NCME, 2014). We collected survey data from 41 student examinees and eight teacher examiners, and we also interviewed the teacher examiners about their perceived validity of the test. Results from the study provided positive evidence for the validity of the speaking test. There were significant associations between student examinees’ speaking test scores, their self-ratings of speaking skills, and their instructors’ end-of-semester ratings of student examinees’ English language proficiency. Both the examinees and examiners also perceived the format and questions to be appropriate and effective. However, the results also revealed some potential issues with the clarity of the rubric and the lack of training for test administration and scoring. These results highlighted the importance of norming and calibration in scoring for the speaking test and entailed practical implications for university-level ESL placement tests.
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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 | 0.141 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".