Developing and evaluating a computerized adaptive testing version of the Word Part Levels Test
Bibliographic record
Abstract
The knowledge about affix plays a vital role in the development of word knowledge and vocabulary acquisition. A test for diagnostic information on the level of affix knowledge would be useful in order to inform the test users of what learners have gained or lacked in this integral component of vocabulary knowledge. This paper reports the development and evaluation of a computerized adaptive testing (CAT) version of the Word Part Levels Test (WPLT), created by Sasao and Webb (2017). The CAT-WPLT was developed to maximize further the effectiveness of the WPLT as a diagnostic test. It was administered to 760 Japanese university EFL (English as a foreign language) learners. The evaluation was based on the comparison of measurement accuracy with the fixed-item version of the WPLT. The results show that the CAT-WPLT can provide test users with diagnostic information on test-taker’s strengths and weaknesses in affix knowledge with smaller number of items and with the same or greater precision than the previous versions of the WPLT. Pedagogical implications for using the CAT-WPLT are discussed along with issues in utilizing computer adaptivity.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".