‘The wisdom of crowds’: When teacher judgments outperform word-frequency as a predictor of students’ vocabulary knowledge
Why this work is in the frame
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Bibliographic record
Abstract
This study investigated the effectiveness of word-frequency and teacher judgments in determining students’ vocabulary knowledge and compared the predictive powers of both approaches when estimating vocabulary knowledge. Twenty-nine second language (L2) Spanish teachers were asked to predict how likely their students would know words from a 216-word Yes/No test that measures knowledge of the first 3,000 words in Spanish. The accuracy of their responses was compared with the results of 1,075 L2 Spanish students who completed the same test. To examine if the results could generalize to other L2 settings, 394 L2 English students completed a 70-word Yes/No test that measures knowledge of the first 14,000 words in English, and 15 L2 English language instructors attempted to predict which words would or would not be recognized. Results showed that for both language contexts, (1) the median teacher rater could assess students’ vocabulary knowledge with an accuracy roughly comparable to frequency, (2) the combination of teachers’ judgments displayed a stronger relationship with students’ performance on the vocabulary test than frequency, since the average of three or more teachers’ ratings improved upon frequency when examined with 1,000 bootstrapped samples, and (3) using teacher judgments and frequency together did not substantially improve the prediction of students’ vocabulary knowledge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it