‘The wisdom of crowds’: When teacher judgments outperform word-frequency as a predictor of students’ vocabulary knowledge
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".