Learning new verbs with known cue words: The relative effects of noun and adverb cues
Bibliographic record
Abstract
Research has shown that learning a known-and-unknown word combination leads to greater learning than learning an unknown word alone (Kasahara, 2010, 2011). These studies found that attaching a known adjective to an unknown noun can help learners remember the unknown noun. Kasahara (2015) found that a known verb can serve as an effective cue to remember an unknown noun in a known-and-unknown combination. To examine useful cues to learn unknown verbs, this study compared verb (unknown) + noun (known) combinations to verb (unknown) + adverb (known) combinations. Additionally, we explored how learners’ vocabulary size would affect the known-and-unknown two-word combination learning to deepen our understanding of the characteristics of students who benefit from combination learning. The participants in each group learned 18 two-word combinations consisting of the same unknown target verbs and different known cues (nouns or adverbs). The participants were provided with a five-minute learning phase and two immediate recall tests: a Single Word Test, to write down the L1 meanings of the targets, and a Combination Test, to write down the L1 meanings of the combinations. The same two tests were administered one week later. The results showed that known nouns were better cues for learning unknown verbs than known adverbs. It was also found that participants with a larger vocabulary size benefited more from two-word combination learning.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".