Mastering the English formula: Fluency development of Japanese learners in a study abroad context
Bibliographic record
Abstract
A common perception in English language education in Japan is that studying English abroad is the way to improve speech proficiency. An important element of speech proficiency is fluency, commonly measured by temporal variables of speech such as speed, pauses, and length of runs of speech. Evidence exists that the use of formulaic sequences, strings, and frames of words with specialized functions, mentally stored and retrieved as single words, is key to fluency. The present study is an examination of the spontaneous speech of four Japanese learners in a study abroad context in Canada. The participants’ narrative retells were analyzed over six months for increased fluency and use of formulaic sequences. The results show that the participants did increase their level of fluency, and that formulaic sequences played an important part in that development. This has implications for English language programs in Japan and other EFL contexts. 海外で英語を学習することにより英語の会話力は向上する、という考えは日本の英語教育の共通認識であるといってよい。会話力における重要な要素の一つは流暢さ（fluency）である。流暢さは通例、速度、ポーズ、発話の長さ等、発話の時間的変異により測定される。これまで行われた研究により、定型表現（formulaic sequences）ー独立した語彙として記憶され使用する際に想起される特定の機能をもった言い回しや単語のまとまりーを使うことが流暢さを増すための鍵となることが明らかになっている。本研究は、カナダで英語を学ぶ４人の日本人学習者の発話を分析、考察したものである。学習者に物語を聞かせ、それを自分の言葉で語らせることによりデータを収集した。期間は６ヶ月間に亘った。データは流暢さと定型表現使用の量について分析した。その結果、確かに学習者は流暢さが増し、さらに定型表現の使用がその上達に重要な役割を果たしたことを示していることが明らかになった。この結果に基づき外国語としての英語教育にさまざまな示唆を行った。
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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 | 0.003 | 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 | 0.001 | 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) | 0.000 | 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".