Vocab SIG: A cognitive semantic approach to L2 learning of phrasal verbs
Bibliographic record
Abstract
This quasi-experimental study investigated the contributions of a paired-associate learning method and a semantic analysis method for enhancing Japanese EFL learners’ knowledge of phrasal verbs. In addition, since dual coding theory argues that basic image schemas of the orientation of particles create opportunities for deeper memory traces, a third treatment was included. It consisted of a semantic analysis along with basic pictures showing the direction of a trajectory in relation to a landmark. The results of the three treatments revealed participants who received the semantic analysis and those who received the semantic analysis plus basic pictures treatment outperformed the paired-associate group on the test. Based on the initial findings, it appears a semantic analysis approach is an effective teaching method that should be used to help learners overcome the confusion experienced when using phrasal verbs. 本論では準実験的研究法を使用し、日本人英語学習者の句動詞に関する語彙知識強化における、対連合学習法 (Paired-Associate Learning Method) と意味論的分析学習法 (Semantic Analysis Method) の貢献度を調査した。さらに、二重符号化理論 (Dual Coding Theory) に基づく主張、すなわち、方向性を示す副詞不変化詞の基本的イメージスキーマによってより深い記憶定着の機会が与えられるという論に基づき、第3の方法を設定した。これは、意味論的分析と共に、目標物と関係づけられた移動軌跡の方向性を示すイメージを与えるものである。以上3種類の方法を行った後、事後テストと遅延事後テストにおいて語彙記憶の保持を測定した結果、意味論的分析を受けた群および、意味論的分析に加えイメージを与えられた被験者群で、対連合学習法の被験者群を上回る結果が示された。この初期調査の結果、意味論的分析が、学習者が句動詞を使用する際に経験する混同を克服する助けとなる効果的な教授法であることが示唆された。
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.004 |
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; both teacher heads agree on what is shown here.
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".