能流暢的使用一個語言常被視為學習一個新語言的最終目標，因此「流暢度」常在語言測驗中是當作其中一個標準。然而，過去的實驗性研究中多說故事為取得口說的方式，受試者也多生活在目標語國家中、且人數不多。本研究受Iwashita, Brown, McNamara, & O’Hagan (2008)之啟發，欲探討以英語為外來言的受試者在托福iBT口說中的流暢度表現，並看是否Iwashita et al.(2008)之研究結果亦適用於台灣學生。主要研究問題如下：
1. 在每種題型中，得分為高/中/低的受試者之時間性測量是否有所差異？
2. 本研究中所使用之時間性測量彼此間之關連性為何？
本研究共有一百位以英語為外來語的台灣學習者參與，他們不是已考過托福iBT就是正在準備。為確保他們身為以英語為外來語的學習者之代表性，他們皆未曾在國外連續待超過六個月。他們經歷一對一的托福iBT口說仿真測驗。兩位英語教師根據托福口說評分標準給予每位受試者每一題型的口說考試成績，並根據其得分將受試者在每個題型中分入三個不同的程度組別中。時間性測量之使用與Iwashita et al.(2008)之研究相同，並多增加一項在Kormos & Dénes (2004)及Towell, Hawkins, & Bazergui (1996)證實為有效之測量方式 – 回答問題時間比。
結果顯示當分組分數依據為流暢及語言使用時，每一題型中，得分高/中/低的受試者在其停頓次數、總停頓時間、語速、平均用字長度及回答時間比的使用上有顯著差異。而當分組分數依據為內容時，在題型一、二、三、五中，得分高/中/低的受試者在同樣五個測量方式之使用上也有顯著差異；而題型四，則只有語速在這三個組別的使用上有差別；題型六，則是語速和平均用字長度有所區別。簡言之，得分較高的受試者停頓次數較少、總停頓時間較短、語速較快、平均用字長度較長、並花較多時間在回答上。而此五個在統計上有顯著差異的時間性測量彼此間也呈現相當程度的關聯性。其中，停頓次數、總停頓時間和回答時間比呈現高度相關，而語速和平均用字長度則呈現中至中高度相關。停頓次數和總停頓時間和得分為負相關，亦即停頓次數越多或總停頓時間越長，得分通常越低；而語速、平均用字長度、回答時間比和得分則為正相關，即說話速度越快、平均用字越多、花越多時間回答問題，得分通常越高。
本研究結果證實以英語為外來語的台灣受試者在測驗的情況下仍在時間性測量方式上呈現出差異，此一結果進一步拓展了我們對「流暢」之了解。在實用性方面，本研究之結果指出部分時間性測量可能抑制考試表現、有些則可增加其口說流暢度，欲參與托福iBT口說之考生將可更明確的注意自己的口說呈現。

Speaking a language fluently is commonly regarded as the ultimate goal of mastering a new language. As a result, test-takers’ fluency is frequently evaluated as part of speaking proficiency tests. Recently, Iwashita, Brown, McNamara, & O’Hagan (2008) reported the relationship between detailed features of speaking performance and holistic scores of around 200 subjects’ performance on the TOEFL iBT prototype speaking tasks. The results showed that vocabulary and fluency had the strongest impact on distinguishing overall speaking proficiency levels. Among the fluency measures, unfilled pauses, total pause time and speech rate showed a significant relationship with proficiency levels. Inspired by Iwashita et al. (2008), this study aims to investigate EFL test-takers’ fluency on simulated TOEFL iBT speaking tasks. To see whether Iwashita’s results would also apply to EFL learners who mainly receive formal on-campus instructions, we used English learners in Taiwan and propose the following questions:
1. How do TOEFL iBT speaking test-takers who have different levels of Delivery, Language Use and Topic Development differ in terms of temporal measures of fluency?
2. What are the relationships among the temporal measures of fluency used in this study?
The participants were 100 Taiwanese EFL learners who either had already taken the TOEFL iBT or had planned to do so by the year 2010. To make sure that these participants were representative of TOEFL iBT test-takers from Taiwan, they had to spend most of their lives in Taiwan and did not spend more than six months abroad. The participants took one simulated TOEFL iBT speaking test in a one-on-one audio-recorded setting. Their performances were rated by two EFL teachers based on the TOEFL iBT rating scales and were grouped into three groups in each task following three speaking constructs – Delivery, Language Use, and Topic Development. The analyses of the speaking data followed Iwashita et al (2008), including the number of filled pauses, unfilled pauses and repair, total pausing time, speech rate, and mean length of run (MLR). One more variable was added – phonation-time ratio (PTR), which was found to be a useful indicator in identifying group differences in Kormos & Denes (2004) and Towell, Hawkins, & Bazergui (1996).
The results indicated that the number of unfilled pauses, total pausing time, speech rate, MLR and PTR successfully differentiated test-takers from three groups based on Delivery and Language Use scores throughout all six task types, and in Task 1, 2, 3, 5 based on Topic Development score. Based on Topic Development score, speech rate was the only temporal measure which showed significant results in Task 4, and speech rate as well as MLR showed significant results in Task 6. On the other hand, the number of filled pauses and repair showed limited results in identifying group differences. Also, the five statistically significant temporal measures highly correlated with one another. The number of unfilled pauses, total pausing time and PTR have strong relationships with each other, with the total pausing time being the opposite of the other two. Speech rate and MLR have strong relationships with each other, while speech rate, unfilled pauses and total pausing time have moderately strong negative relationship with each other. Finally, filled pauses and repair seemed to have no obvious relationship with the rest of the temporal measures.
Previous research on fluency has tended to focus on ESL context or use narrative tasks in non-testing contexts. Not much has been done with monologic tasks in a testing context. Thus, results from this study provided further information on EFL learners’ fluency in a testing context. Additionally, the results from this study could assist test-takers to pay more attention to the significant features attributing to language flow and do better in their actual TOEFL iBT performance.

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