Two Formats of Word Association Tasks: A Study of Depth of Word Knowledge
Bibliographic record
Abstract
Vocabulary development is an essential goal in any language teaching program, and considering the multidimensional nature of this construct, achieving this goal needs effective assessment of all dimensions of word knowledge, i.e. breadth, depth and accessibility of word knowledge. Most of the current vocabulary assessment tools measure the breadth dimension of vocabulary (Christ, 2011). However, there have been studies which have developed Selective or Productive word association tasks (WAT) to measure depth of word knowledge (Meara & Fitzpatrick, 2000; Read, 1998). This study used both selective and productive WAT tasks to measure depth of word knowledge in 82 elementary and 71 advanced EFL learners to explore which format is better for assessing deep word knowledge for each group. Results showed that elementary learners did better in selective format while advanced learners acted better in productive format.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".