Investigating the Construct Validity of an ESL Test for Young Learners
Bibliographic record
Abstract
This study investigated the construct validity and reliability of the GEPT-Kids, which is composed of 25 listening items and 30 reading items. Data were collected from 742 test takers from Grades 5 and 6 from eight Chinese-speaking schools in Beijing, Shanghai, Guangzhou, Hong Kong, Macau, Taipei, Taichung, and Kaohsiung. Two CFA models (Model 1: a one-factor model with a general language proficiency construct; Model 2: a two-factor model with listening and reading constructs based on the test design) were proposed and analyzed using AMOS 24.0 to reveal whether the internal structure of the test reflected the test developers’ design for the test. The results showed Model 1 provided an adequate explanation of the data as most of the model fit indices were acceptable, and Model 2 showed that there was a lack of discriminant validity between listening and reading because they were too highly correlated although the fit indices were high. While both models can be considered acceptable based on fit indices, and Model 1 is more parsimonious than Model 2. To sum, the main finding was that GEPT-Kids is valid in terms of score interpretation of young learners’ overall language ability but may lack discrimination in terms of assessing subskills of English language proficiency. Therefore, findings from this study suggest that GEPT-Kids is acceptable as a general language proficiency test but should be used with caveats while using test scores in terms of listening and reading in the classroom teaching-learning context.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".