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Fluency across modes: towards a more comprehensive analysis of L2 fluency
Oral Presentation[SYMP36] Fluency as a multilingual practice: Concepts and challenges08:30 AM - 11:30 AM (Europe/Amsterdam) 2023/07/20 06:30:00 UTC - 2024/07/20 09:30:00 UTC
Fluency is a multifaceted concept used in cognitive sciences and second language (L2) teaching and learning research. Spoken fluency is considered to have cognitive, utterance and perceived fluency dimensions (Segalowitz, 2010; see also Lintunen et al., 2020a), corresponding in written fluency studies to different measures for the writing process and the final product (Cislaru & Olive, 2018; Kowal, 2014; Mutta, 2020). Recently, L2 fluency studies have been frequent, but researchers have called for more comprehensive analyses, for instance, by combining different data sets to examine fluency profiles across spoken and written modes (Lintunen et al., 2020b). Moreover, comparing the L2 learner's fluency profile in their native language (L1) and L2 has been found important to reveal idiosyncratic patterns that are more user-specific rather than part of L2 development. This presentation combines multimodal analyses of spoken and written fluency and contributes to increasing our knowledge of individual language users' and learners' spoken and written fluency profiles in their L1 and L2. Our data were combined from two larger research projects on spoken and written fluency. We identified 11 university students, who participated in both projects. They performed the tasks in both L1 (Finnish) and L2 (English). The spoken tasks consisted of monologue picture description tasks, and the written tasks were short essays. To collect the written data, we used the graph theory based keystroke logging software GGXLog with a visualization function. To compare the fluency profiles, we analyzed the spoken (e.g. pause lengths, repairs, mean length of runs) and written data measures (e.g. pause times, corrections, length of bursts). Six participants also verbalized on their L1 and L2 writing process and explained their strategic choices. During the presentation, we will present some fluency profiles based on the learners' spoken and written productions. The preliminary results show that some students are fluent writers both in L1 and L2, which is related to their high proficiency level in L2; they seem to be exposed to English in informal and formal contexts in many ways. The spoken data revealed fluent speech in two languages, with some links in speed, breakdown and repair fluency measures across languages and modes.
References Cislaru, G. & T. Olive (2018). Le processus de textualisation. Analyse des unités linguistiques de performance écrite. Louvain-la-Neuve: De Boeck Supérieur. Kowal, I. (2014). Fluency in second language writing: A developmental perspective. Studia Linguistica Universitatis Iagellonicae Cracoviensis, 131, 229–246. Lintunen, P., M. Mutta & P. Peltonen (2020a). Defining fluency in L2 learning and use. In P. Lintunen, M. Mutta & P. Peltonen (Eds.) Fluency in L2 learning and use. Bristol: Multilingual Matters, 1–15. Lintunen, P., M. Mutta & P. Peltonen (2020b). Synthesising approaches to second language fluency: Implications and future directions. In P. Lintunen, M. Mutta & P. Peltonen (Eds.) Fluency in L2 learning and use. Bristol: Multilingual Matters, 186–201. Mutta, M. (2020). L2 fluency and writer profiles. In P. Lintunen, M. Mutta, P. Peltonen (Eds.) Fluency in L2 learning and use. Bristol: Multilingual Matters, 63–80. Segalowitz, N. (2010). The cognitive bases of second language fluency. New York: Routledge.
Presenters Maarit Mutta Associate Professor (PhD), University Of Turku
Collocational processing as a measure of cognitive fluency: How is it linked to utterance fluency?
Oral Presentation[SYMP36] Fluency as a multilingual practice: Concepts and challenges08:30 AM - 11:30 AM (Europe/Amsterdam) 2023/07/20 06:30:00 UTC - 2024/07/20 09:30:00 UTC
Second language oral fluency research has suggested that collocations in speech (e.g., kill time) are linked to efficient speech production, that is, utterance fluency (UF)(Tavakoli & Uchihara, 2019). Collocations appear to be processed as wholes, taking advantage of linguistic encoding in a speech production (Kormos, 2006). Since Segalowitz's (2010) framework of the subcomponents of oral fluency, scholars in this domain have sought the interrelationship between them. Recent studies highlight what underlying linguistic knowledge and processing (cognitive fluency: CF) lead to UF (e.g., Suzuki & Kormos, 2022), and found that vocabulary knowledge and processing speed greatly contribute to speed and breakdown fluency. What is missing, however, is the inclusion of collocational processing into CF measures. The only study examining the link between collocational processing and UF is Koizumi and In'nami (2013) but they did not examine breakdown fluency. To examine the connection between collocational processing speed and UF, Japanese undergraduate students (N = 105) performed a phrasal acceptability judgement task where reaction times in judgement of collocations using binary responses were measured. The collocation items consisted of 42 critical stimuli with the three frequency bins (high, mid, and low) and 42 filler stimuli. The adjective + noun collocations were pooled from Ackermann and Chen's (2013) Academic Collocation List. The mutual information score, length, congruency, adjective and noun frequencies of items in the three frequency bins were all closely matched. Speech samples were elicited by an argumentative speech task, and UF measures were computed based on Tavakoli and Skehan's (2005) framework of speed (articulation rate), breakdown (mid/end-clause silent pause ratio/duration; filled pause ratio), and repair fluency (disfluency ratio). Correlation analyses were performed to examine the relationship between collocational processing speed (reaction times) and UF measures. The result showed that reaction times in high-frequency collocations had the largest coefficient among the three frequency levels for articulation rate (rho = -.287), mid-clause silent pause ratio (rho = .345), and end-clause silent pause ratio (rho = .334). The result indicated a potential contribution of collocational processing speed to efficient speech production.
Ackermann, K., & Chen, Y. H. (2013). Developing the Academic Collocation List (ACL) - A corpus-driven and expert-judged approach. Journal of English for Academic Purposes, 12(4), 235–247. https://doi.org/10.1016/j.jeap.2013.08.002 Koizumi, R., & In'nami, Y. (2013). Vocabulary knowledge and speaking proficiency among second language learners from novice to intermediate levels. Journal of Language Teaching and Research, 4(5), 900–913. https://doi.org/10.4304/jltr.4.5.900-913 Kormos, J. (2006). Speech production and second language acquisition. Lawrence Erlbaum. Segalowitz, N. (2010). Cognitive bases of second language fluency. Routledge. Suzuki, S., & Kormos, J. (2022). The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency. Studies in Second Language Acquisition, 1–27. https://doi.org/10.1017/S0272263121000899 Tavakoli, P., & Skehan, P. (2005). Strategic planning, task structure and performance testing. In R. Ellis (Ed.), Planning and task performance in a second language (pp. 239–277). John Benjamins. Tavakoli, P., & Uchihara, T. (2019). To what extent are multiword sequences associated with oral fluency? Language Learning, 70(2), 506–547. https://doi.org/10.1111/lang.12384
What difference does spacing make in fluency practice? An investigation of short- vs. long-spaced practice
Oral Presentation[SYMP36] Fluency as a multilingual practice: Concepts and challenges08:30 AM - 11:30 AM (Europe/Amsterdam) 2023/07/20 06:30:00 UTC - 2024/07/20 09:30:00 UTC
There has been a growing interest in examining the role of spacing in repeated task performance and L2 speech fluency development (e.g., Bui et al., 2019; Suzuki & Hanzawa, 2021). Although research in cognitive psychology suggests that spaced learning is generally more beneficial than massed learning (Wiseheart et al., 2019), previous studies in L2 fluency research have shown mixed results. In this line of research, it is still particularly unclear to what extent spacing influences L2 learners' long-term fluency development. Importantly, no research to date has investigated the effects of spacing on L2 fluency development by systematically manipulating the ISI–RI ratio (i.e., the ratio of the interval between practice sessions [intersession interval, ISI] to the interval between the last practice session and the posttest [retention interval, RI]). An investigation of specified ISI–RI ratios is necessary to gain a better understanding of the effects of spacing on L2 speech fluency development, and how the research findings from cognitive psychology can be applied to the rather complex skill of L2 speaking. The current study aimed to examine the effects of spacing on L2 learners' speech fluency development by using the ISI–RI ratios of 10–30%, an optimal range suggested by previous research in cognitive psychology (Rohrer & Pashler, 2007). In this study, 116 Japanese university students were randomly assigned to one of four groups, which consisted of two experimental groups (a short-spaced group [1-day ISI] and a long-spaced group [7-day ISI]) and two control groups. The experimental groups engaged in four practice sessions while the control groups only took the three tests (pretest, posttest, delayed posttest) which followed the same schedule as each corresponding experimental group. The results overall showed an advantage of long-spaced practice over no practice (i.e., control group) and short-spaced practice especially on the delayed posttest, demonstrating greater retention of enhanced fluency performance (e.g., faster speech rate, shorter mid-clause pauses). In this presentation, the presenters will focus on the results from the practice sessions to elucidate how manipulating the timing of repeated practice might influence L2 learners' fluency development over time. The present findings contribute to the existing body of L2 research by yielding insights into the role of spacing in optimizing fluency practice.
Bui, G., Ahmadian, M. J., & Hunter, A.-M. (2019). Spacing effects on repeated L2 task performance. System, 81, 1–13. https://doi.org/10.1016/j.system.2018.12.006 Rohrer, D., & Pashler, H. (2007). Increasing retention without increasing study time. Psychological Science, 16(4), 183–186. https://doi.org/10.1111/j.1467-8721.2007.00500.x Suzuki, Y., & Hanzawa, K. (2021). Massed task repetition is a double-edged sword for fluency development: An ESL classroom study. Studies in Second Language Acquisition, 1–26. https://doi.org/10.1017/S0272263121000358 Wiseheart, M., Küpper-Tetzel, C. E., Weston, T., Kim, A. S. N., Kapler, I. V, & Foot-Seymour, V. (2019). Enhancing the Quality of Student Learning Using Distributed Practice. In J. Dunlosky & K. Rawson (Eds.), The Cambridge Handbook of Cognition and Education (pp. 550–584). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108235631.023
Presenters Joe Kakitani PhD Student, Lancaster University Co-authors
The effects of proficiency on oral production fluency
Oral Presentation[SYMP36] Fluency as a multilingual practice: Concepts and challenges08:30 AM - 11:30 AM (Europe/Amsterdam) 2023/07/20 06:30:00 UTC - 2024/07/20 09:30:00 UTC
Speech fluency, which includes linguistic, psycholinguistic, and sociolinguistic elements, is a complicated phenomenon that interacts with other performance-related factors (Kormos, 2006; Lennon, 2000; Segalowitz, 2000, 2010). Fluency features are reported to be among the best indicators of L2 proficiency development (De Jong, 2018; Révész et al., 2016). That is, some fluency features can likely be reduced during the development of proficiency. The present study aims to find out the extent to which proficiency can affect speech fluency of lower and higher proficiency L2 learners. The data were collected from 66 L2 learners who narrated two picture stories. Proficiency was assessed using elicited imitation test(EIT) which is a testing tool that has been validated in several L2 studies (e.g., Gaillard & Tremblay, 2016; Wu & Ortega, 2013). The data were transcribed and coded for measures of fluency which included speed (syllable per minute), breakdown (frequency and length of filled and silent pauses, repetition, and hesitation) and repair features (self-correction). A series of one-way ANOVA was conducted to find out whether proficiency can affect fluency production. The results indicated that there were differences between lower and higher proficiency learners in certain fluency measures. This suggests that proficiency development may likely be reflected in certain fluency features. The findings have important implications for L2 fluency research and measurement.
An Investigation of Linguistic Features of Fluency by English Speakers with Different Proficiency Levels
Oral Presentation[SYMP36] Fluency as a multilingual practice: Concepts and challenges08:30 AM - 11:30 AM (Europe/Amsterdam) 2023/07/20 06:30:00 UTC - 2024/07/20 09:30:00 UTC
This study aims to investigate the linguistic features (LFs) of fluency exhibited by English learners with different proficiency levels (PLs). 39 English learners were divided into four groups based on their International English Language Testing System (IELTS) speaking scores: low-level (L) at band 5.0-5.5; medium-level (M) at band 6.0-6.5; medium-high-level (M-H) at band 7.0-7.5; and high-level (H) at band 8.0-9.0. 29 LFs were measured, including length of the speech (LS), repetition (R), self-correction (SC), speech rate (SR), and pausing (P). 14 LFs were identified significantly different across the four PLs. Learners with higher PL produced more words. L and M learners had lower SR; however, M-H learners produced the highest. L learners repeated an utterance more frequently and produced the greatest number and longest total duration of P. The higher the PL a learner has, the less filled pauses (FP) he/she used. L and M learners had more silent pauses in the middle of an utterance (SPMs) but fewer silent pauses at the end (SPEs) (>0.4s). LS, R, mean, maximum, and minimum SR, total number and duration of P, FP, and SPM are significant linguistic predictors of fluency.
Fluency is one of the important indicators of oral PLs in second language (L2) tests, such as the IELTS (e.g., Fulcher, 2003). It plays an important role in L2 acquisition and is highlighted as a significant predictor of a speaker's L2 PL (Wright & Tavakoli, 2016). Previous studies pointed out the complex nature of fluency (Tavakoli & Wright, 2020) and reported LFs such as speed of an utterance, pauses, and hesitations could reflect speakers' utterance fluency (Segalowitz, 2016). Breakdown (e.g., pausing), speed (e.g., how fast), and repair (e.g., correction) are indicated as three measures of fluency (e.g., Lahmann et al., 2017). However, no evidence illustrates which measures could best describe the fluency of different PLs and can help to distinguish one PL from the other (Suzuki & Kormos, 2022). The current study analyzed 29 LFs and fixed the research gaps.
According to the descriptors of the IELTS speaking test (https://www.ielts.org/-/media/pdfs/speaking-band-descriptors.ashx), six main features of fluency, including LS, SR, pauses, hesitations, Rs, and SCs, are mainly evaluated by examiners. SR can be considered as the speed, pauses and hesitations as the breakdown, and Rs and SCs as the repair in Lahmann et al. (2017). The current study added one more measure, length.
We measured four sub-features of LS, three sub-features of SR, twenty sub-features of pauses and hesitations, and the total number of R and SC. FPs (e.g., um), SPMs, and SPEs are measured to investigate pauses and hestitations. In this study, SPMs and SPEs are categorized into shorter than 0.4s and equal to or longer than 0.4s. The different performances on the 29 LFs across the four PLs were compared and significant linguistic predictors of English fluency were identified. This study provides great pedagogical value. Currently, teachers' understanding of fluency is in a broad sense, and there is a gap between fluency research and real fluency teaching (Tavakoli & Hunter, 2018). Teachers should focus on the results of this study and give prior instructions and feedback on the 14 features to improve learners' English fluency.
References
Fulcher, G. (2003). Testing second language speaking. Harlow, North Dakota: Pearson Education.
Lahmann, C., Steinkrauss, R., & Schmid, M. S. (2017). Speed, breakdown, and repair: An investigation of fluency in long-term second-language speakers of English. International Journal of Bilingualism, 21(2), 228-242.
Segalowitz, N. (2016). Second language fluency and its underlying cognitive and social determinants. International Review of Applied Linguistics in Language Teaching, 54(2), 79-95.
Suzuki, S., & Kormos, J. (2022). The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency. Studies in Second Language Acquisition, 1-27.
Tavakoli, P., & Hunter, A. M. (2018). Is fluency being 'neglected'in the classroom? Teacher understanding of fluency and related classroom practices. Language Teaching Research, 22(3), 330-349.
Tavakoli, P., & Wright, C. (2020). Second language speech fluency: From research to practice. Cambridge University Press.
Wright, C., & Tavakoli, P. (2016). New directions and developments in defining, analyzing and measuring L2 speech fluency. International Review of Applied Linguistics in Language Teaching, 54(2), 73-77.
Maarit Mutta
Pauliina Peltonen
Kotaro Takizawa
Joe Kakitani