Investigating Content and Face Validity of English Language Placement Test Designed by Colleges of Applied Sciences
Bibliographic record
Abstract
An English placement test (PT) is an essential component of any foundation program. It helps place students into their suitable language proficiency level so that they do not spend time learning materials below or above their levels. It also helps teachers to prepare teaching materials to students of similar levels (Brown, 2004; Illinois, 2012). This paper aims to investigate the extent to which the PT used at Colleges of Applied Sciences (CAS) is achieving this goal by exploring teachers’ and students’ perceptions of the current exam via questionnaires and interviews. Furthermore, it examines the format and content of the PT and students’ PT score against their mid-term score. It was found that face validity of CAS PT ranged from low (teachers) to moderate (students). The majority of teachers and students emphasized the importance of including the listening and speaking components in the test. Moreover, a modified version of the reading section of the test needs to be incorporated into the test. Another suggested using a valid certified computerized test. For future research, it is recommended to design a new test, taking into consideration the findings of this research and pilot it to test its effectiveness. Furthermore, an analytical test of the current marking criteria is essential to check its clarity and consistency.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".