The internal consistency and accuracy of automatically scored written receptive meaning-recall data: A preliminary study
Bibliographic record
Abstract
Vocableveltest.org is a testing platform on which users can create online self-marking meaning-recall (reading or listening) and form-recall (typing) tests that address a number of limitations of the existing vocabulary level tests and vocabulary size tests. A major limitation of many existing vocabulary tests is the written receptive meaning-recognition (multiple-choice or matching) format which is associated with increased error due to guessing and decreased power to measure the type of vocabulary knowledge suitable for reading practice (McLean et al., 2020, Stewart et al., 2021a, Stoeckel et al., 2021), despite being designed for this purpose (Nation, 2012, Schmitt et al., 2020, Webb et al., 2017). Conversely, scoring meaning-recall tests by hand is labour-intensive, and the internal consistency and accuracy of automatically marked data are unknown. Thus, this study investigated the internal consistency and accuracy of automatically marked responses of 98 words from the fifth 100 most frequent words of English. This study tested for knowledge of high-frequency words as a more robust test of the marking system, as these words possess multiple-meaning senses, making their automatic marking problematic. Furthermore, the predicted limited range of learners’ knowledge of these 98 words was expected to result in data of a low internal consistency. However, the automatically marked data had a high internal consistency (Cronbach’s α = 0.868) and was 98% similar to human marked meaning-recall responses.
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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 | 0.037 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".