【Research Brief】

Designing a Mobile Application to Track Spoken Fluency
Development
Heather Woodward & Charu Gupta
Abstract
English discussion class (EDC) course designers state that the most important language-based objective of EDC is toimprove students’ spoken fluency (Hurling, 2012). In general, the students do seem to be increasing their speaking
speed; however, designers have not developed a method to document their progress across the semester. Meanwhile,mobile-assisted language learning (MALL) applications can enhance personalization and feedback, but theseapplications have been traditionally designed to build students’ second language (L2) vocabulary rather than their L2
fluency (Heil et al., 2016). In Spring 2022, we repurposed an existing mobile application that analyzes speech rate
(words per minute) to provide more personalized feedback. The problem with the application is that it does not havefeatures that reflect current spoken fluency research. Therefore, we review the literature on spoken fluency research,then design an application that collects and records students’ speech rate (syllables per minute), mid-clause pause
frequency, self-assessment, and peer-assessment.
Keywords: EFL, MALL, speaking, 4/3/2

Introduction
The goal of language learning for many Japanese students is the ability to speak fluently;
however, achieving fluency is challenging in the context of Japan where there are few chances forstudents to use the foreign language owing in part to the washback effects from university entrance
exams, which have historically not included a speaking component (Garside, 2020). Japanese
students also strive for accuracy and perfection, so they spend time correcting their syntactic orphonetic mistakes and this extra time can decrease their spoken fluency (Watanabe & Long, 2019).
Without incorporating fluency-building activities into the L2 classroom, communicative language
teaching is limited in scope (Gatbonton & Segalowitz, 1988), and as a result, students’ learning might
not be available during the typical demands of real-world communication (Hurling, 2012). Not
meeting typical demands of real-world communication means that students are unable to holdlisteners’ attention or save face (Lennon, 2000).
Applied linguistics researchers analyze three aspects of spoken fluency -- speed, breakdown, andrepair. The first aspect, speed, equates to the density and flow of speech. The second aspect,
breakdown, consists of hesitations and pauses, and the last aspect, repair, comprises corrections,reformations, and repetitions (Skehan, 2003; Tavakoli & Skehan, 2005). For example, researchers
might calculate the speed of the L2 spoken performance by using the average number of syllables
per minute, then analyze the breakdowns of an L2 spoken performance by calculating the mean
length of pauses. The process of transcribing and analyzing students’ L2 spoken performances is tootime consuming to be widely incorporated by second language (L2) instructors in the classroom. In
addition, conditions such as background noises of other students speaking make collecting andanalyzing data very challenging.
However, quantitative measures such as the average number of syllables per minute are limited

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in describing spoken fluency performance because they are unable to account for other mediating
factors such as students’ first language (L1) speaking styles, personalities, or socio-pragmaticconsiderations (Tavakoli & Wright, 2020). One possibility is to create a mobile application thatincorporates both quantitative measurements and qualitative judgments while also adding instructionalfeatures to enhance students’ metalinguistic awareness of spoken fluency to provide more
personalized feedback. By using a filter, the background noises of other students in the classroom
might be reduced enough to collect information about students’ spoken fluency performance. In thisresearch brief, we first review literature on the goals of English discussion class (EDC). Afterward,
we investigate research on applied linguistics to determine the most appropriate objective
measurements to use to measure spoken fluency. We also discuss gamification and personalization,and then present a mobile application design that includes these features.

Literature Review
English Discussion Class
EDC is required for first-year students at Rikkyo University to improve their communication
skills, academic discussion skills, and spoken fluency (Hurling, 2012). Examples of communication
skills along with a corresponding formulaic sequence include clarification (Can you repeat that?),comprehension (Do you understand?), and paraphrasing (In other words, do you mean…?) (Kita etal., 2022). Academic discussion skills include joining a discussion (Can I start?), viewpoints (Howabout from the perspective of...?), and sources of information (How do you know about that?) (Kita etal., 2022). Students are separated into four EDC levels according to their test of English forinternational communication (TOEIC) scores. The four EDC levels are: Level 1 (TOEIC scores 680
or above; common European framework of reference for languages (CEFR): B2 and above), Level 2(480-679; B1-B2), Level 3 (280-479; A2-B1), and Level 4 (279 or below; A1-A2). Additionally, thecourse is for 14 weeks, has 10 students per class, and students meet once a week for 100 minutes.
During each lesson, five students participate in a 20-minute discussion while instructors assess
students’ participation and use of formulaic sequences (English Discussion Committee Handbook,2022). On the rubric, students receive a 4, or superior, for academic discussion skills if they use themquickly, appropriately, and without looking in the textbook. Quickness relates to the spoken fluency’s
aspects of speed and breakdown. They receive a 3, or good, if students use discussion skills, butcannot do so from memory (English Discussion Committee Syllabus, 2022). The other scale
descriptors of none, poor, and fair, do not explicitly refer to students’ spoken fluency. In addition, thetwo other constructs assessed—communication skills and participation—also do not explicitly referto spoken fluency. However, speaking quickly during the discussion performance test gives others
time to participate and use discussion and communication skills. The rubric is the same for all EDC
levels (i.e., from Level 1 to 4). Although spoken fluency is the most important language-based goal of
EDC according to course developers, the only fluency assessment of students consists of the extent
to which instructors perceive their fluency of discussion skills in the context of group discussion
performance.

4/3/2 Activity and Other Fluency Practices in EDC
A modified version of Maurice’s (1983) 4/3/2 activity called 3/2/1 is used during every EDC

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lesson. 3/2/1 incorporates task features of repetition and time pressure, and these task features havebeen shown to boost students’ spoken fluency (e.g., Arevat & Nation, 1991; Boers, 2014; Garside,
2020; de Jong & Perfetti, 2011; Molina Barriga & Briesmaster, 2017; Thai & Boers, 2016; Tran &Saito, 2021). For this modified version of 4/3/2, students deliver three iterations of their monologues.
Speakers change listeners after each iteration. 3/2/1 takes 15 minutes and with 10 students per class,5 students speak simultaneously. For theoretical support, course designers cite Schmidt (1992) who
uses Anderson’s (1989) adaptive control of thought (ACT) theory (Hurling, 2012). In ACT, fluency
equates to automatic processing of proceduralized knowledge (i.e., knowing how to use the L2 witheffortless efficiency) and after repeated, meaningful practice, in theory, students can retrieve
knowledge more quickly and smoothly than before.
Not only do students use 4/3/2 in EDC to improve spoken fluency, but course designers havealso adopted other practices to help students boost spoken fluency. EDC students a) use formulaic
sequences in communicative contexts; b) spend time pre-task planning to conceptualize andformulate their ideas; c) repeat the task; and d) learn about spoken fluency with metalinguistic
awareness-raising activities (See Tavakoli & Hunter, 2018). For a) use formulaic sequences, students
incorporate discussion and communication skill phrases into their discussions to boost fluency. Forb) pre-tasking planning, students have preparation time before their discussions to think of theirideas. For c) task repetition, students repeat the discussion with slightly different topics for thepractice and perform two discussions per lesson. For d) metalinguistic awareness-raising activities,
Curran (2019), an EDC instructor, uses self-reflection activities after the 3/2/1. For instance, he asks,“Did you speak more quickly?” and “Did you have to pause?” Awareness-raising activities can help
students achieve a greater understanding of spoken fluency and the ways to improve it (Curran,2019). EDC course designers incorporate these types of activities to help students improve theirspoken fluency during the course, but there is no way to measure students’ progress across thesemester so instructors might find it challenging to provide personalized feedback or support for
students. Next, we explain spoken fluency research findings to design a mobile application that can
track its development.

Spoken Fluency Research Findings
Spoken fluency is multidimensional so to explain research findings more precisely, Segalowitz(2010, 2016) separates spoken fluency into three interconnected dimensions: cognitive, utterance,and perceived. Cognitive fluency is the fluidity of underlying cognitive mechanisms that cause L2
speech acts (Segalowitz, 2016) and is operationalized as reaction time speed and reaction timestability (Segalowitz & Segalowitz, 1993). Utterance fluency refers to observable speech production(i.e., speed, breakdown, and repair measurements). Perceived fluency is raters’ subjective judgmentsof overall spoken fluency (Segalowitz, 2010, 2016). His model emphasizes that fluency is not only a
psycholinguistic construct, but also an interactional one, which means that listeners also play animportant role in determining spoken fluency (Tavakoli & Wright, 2020). Fluency is regarded byexaminers and raters as the most difficult aspect of L2 spoken performance to judge (Kang et al.,2019). Therefore, determining which aspects of utterance and perceived fluency should be evaluated
and which features of utterance fluency should distinguish descriptor levels are key decisions forboth human and automated rating systems (Tavakoli & Wright, 2020). In the next sections, weinvestigate the applied linguistics research to investigate these key decisions for designing ourmobile application.

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What Aspects of Utterance Fluency Should be Evaluated on the Application?
A meta-analysis of 22 studies by Suzuki et al. (2021) investigates the relationship between
aspects of utterance fluency compared with listener-based perceived ratings of monologues.
Utterance fluency measurements should be strongly associated with perceived fluency to be areliable source of information for assessments. Results show that listeners’ perceived fluency ratings
are strongly associated with pause frequency and speed, moderately associated with pause duration,
and weakly associated with repair fluency (Suzuki et al., 2021). Repair fluency (corrections,reformulations, and repetitions) can strongly relate to speaking style preferences (Kahng, 2014;
Tavakoli & Skehan, 2005). Meanwhile, composite measurements show the strongest effect sizes
compared with any pure speed, repair, or breakdown measures (Suzuki et al., 2021). Composite
measurements incorporate two or more pure measurements. For example, one composite
measurement is speech rate, which is calculated by dividing the total number of syllables in a given
speech sample by the entire time it takes to create the speech sample in seconds, including pause
time, and then multiplying by 60 (Kormos & Dénes, 2004). A pure speed measurement is articulation
rate, which is similar to speech rate, but does not include pauses.
Pause location strongly affects perceived fluency as well. Suzuki and Kormos (2020) examine L2
argumentative speaking performance judged by 10 native English-speakers inexperienced raters. For
this task, perceived fluency is more strongly associated with mid-clause pausing frequency than withother aspects (Suzuki & Kormos, 2020). For instance, between-clause pausing is “She doesn’t likedoing dishes, (pause) but you don’t either,” which can indicate topic change or an idea shift.
Mid-clause pausing is “She doesn’t like doing (pause) dishes.” L2 speakers pause mid-clause more
frequently as compared with L1 speakers, which suggests that at least some L2 speakers’ mid-clausepausing reflects L2 proficiency gaps associated with online planning, reformulation, and replacement(Skehan et al., 2016; Tavakoli, 2010; Tavakoli & Skehan, 2005). In Suzuki et al.’s (2021) meta-analysis,
researchers find that mid-clause pausing is even more strongly associated with perceived fluency
than pure speed measurements, but slightly less associated than composite fluency measurements
such as speech rate. This suggests that speech rate and mid-clause pausing are some of the bestutterance fluency measurements for tracking students’ spoken fluency progress across the semester.

What Utterance Fluency Features Should Distinguish Descriptor Levels?
EDC does not distinguish spoken fluency features for Levels 1-4, so one question we ask is, if weincorporate descriptor levels on the spoken fluency application, which features should distinguishlevels? The problem is as Bradlow et al. (2017) conclude “individual variability in L2 spoken language
production may be best understood within the context of individual variability in L1 spoken language
production.” For instance, repairs (repetition and pausing) can be indicative of L1 speaking stylerather than L2-specific disfluencies (Bosker et al., 2012; Duran-Karaoz & Tavakoli, 2020). Additionally,
Bradlow et al. (2017) write that in absolute terms, students speak their L2 slower than their L1, andL1 speaking rate can significantly predict L2 speaking rate. That is, faster L1 speakers are also fasterL2 speakers. Shrosbree (2020) also finds that participants with TOEIC scores over 900 have positive
correlations for 7 of the 10 spoken fluency measures; however, in absolute terms, they speak their L2
slower than L1 (Shrosbree, 2020). Yet, test makers seldomly ask raters to consider students’ L1
speaking style when assessing (Segalowitz, 2016).
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factors include background noise from others, ease or familiarity with topics, and interlocutors(Tavakoli & Wright, 2020). Segalowitz argued for the importance of assessing sources of variability
that are not related specifically to L2 disfluencies but that characterize a person’s general performancein the given testing conditions (Segalowitz, 2010). For these reasons, zero-stakes assessments,
conducted for formative purposes, rather than summative, can include individualized baselines toaccount for students’ speaking style, and this kind of assessment can also take external factors into
consideration that might be sources of variability. One example is students journaling about theirexperiences to discuss different external factors. Therefore, instead of descriptors, an alternative
solution is for the mobile application to suggest a small, incremental mid-clause pause frequency andspeech rate goals that take into consideration internal factors such as students’ L1 speaking
performance and external factors such as topic familiarity.

Should Human Raters be Included With a Computer Automated Rating System?
Another design decision for a mobile application that tracks spoken fluency is whether to havethe application fully automated or to have an element of human ratings. With regard to human raters,de Jong (2018) states that the specificity and amount of instruction can enable human raters to focus
on certain fluency features such as pause frequency, pause location, and speed. Moreover, raters
might even be innately sensitive to pause location with an understanding that mid-clause pausing ismore likely a reflection of decrease in cognitive fluency (Kahng, 2018). Automated speech evaluation
alone might not be suitable for spoken fluency assessments because the goal of measuring students’
L2 ability is to determine the quality of test takers’ verbal communication with humans, not withmachines (Ginther et al., 2010). For these reasons, listener-based responsibilities during 3/2/1 suchas tracking mid-clause pausing and peer-evaluation of comprehensibility are also important. Human
raters might increase the reliability of fluency judgments because students perceive other reasons for
pausing such as socio-pragmatic considerations (e.g., if students are telling a sad story, they mightpause for non-L2 specific disfluent reasons).
Saeki et al. (2021) have designed an AI conversational agent called InteLLA that adapts its
interview to assess L2 spoken proficiency. InteLLA’s fluency aspect detects speakers’ pauses,
annotates pause locations (e.g., of mid-clause pauses), and also notes disfluency markers such as
fillers and false starts. To mimic human ratings, researchers assign different weights to the fluency’s
temporal features based on associations between utterance fluency and perceived fluency. InteLLA’s
accuracy in replicating L2 perceived fluency is 60% using the CEFR level classification system (Saeki
et al., 2021). Some of these features might be also useful for a mobile application. The technology cannote disfluency markers such as fillers, but for the annotations of pauses, we wonder whether
listener-based judgments of mid-clause pausing might be more reliable than an automated systembecause listeners might have greater insight into the reason for the pause than an automated system.
As the mobile application’s purpose is different from InteLLA’s purpose, we can also add otherhuman-rating judgments such as self-assessment and instructor-based assessments.

Gamification and Personalization
We have also reviewed literature on gamification and personalization. Implementing technologydriven play and competition is becoming increasingly popular in the classroom (Chen et al., 2021;
Dehghanzadeh et al., 2021; Flores, 2015; Huang & Soman, 2013). Researchers have found that164

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students are more motivated and engaged, while also feeling less anxious about using their L2 when
gaming elements such as points, medals, badges, or progress tracking are introduced (Dehghanzadeh
et al., 2021; Flores, 2015; Huang & Soman, 2013). Students are more inclined to compete with theirclassmates and themselves to earn medals and increase their ranking (Arce & Valdivia, 2020). Rego(2015) specifies important elements of gamification such as having
a. clear goals
b. rulesc. an accessible and aesthetic user interface
d. collaboration and interaction aspects
e. rewards (e.g., medals or ranking)f. tracking of students’ progress
g. performance feedback
We have included features a)–g) in the mobile application design and we discuss these elementsin the next section. In addition to Rego’s (2015) advantages, gamification has also been lauded for its
ability to incorporate personalized learning (Chen et al., 2021), as personalized activities are usuallymore meaningful, relevant, and self-initiated. For technology-based personalization, Kukulska-Hulme(2016) emphasizes that with continued L2 practice, personalization can help students to a) identify
their needs, b) develop greater awareness of the learning process, and c) monitor their progress. For
monitoring their progress students can reflect on and the analyze their performance (Dehghanzadeh
et al., 2021). Heil et al. (2016) review 50 language learning-related applications and find that whilemobile applications track progress and adjust difficulty levels, most adjustments are minor and notalways implemented by students. The feedback given also does not provide reasons for incorrect
responses or how to improve performance (Heil et al., 2016). To navigate the aforementioned
limitations, instructors should include their feedback (Chen et al., 2021).

Application Design
In this section, we describe the functions of the application and connect it to the literature
review. To measure students’ spoken fluency, they record themselves for each round of 3/2/1. Whilethey are speaking, the application is collecting data on average number of syllables untrimmed,
maximum speed, total number of syllable, and fillers. Their partners listen to their monologues whiletallying their mid-clause pauses for each round. If students reach or exceed their fluency goal, they
have a “Congrats” message. Once speakers finish their monologues, their listening partners rate thespeaker on comprehensibility (i.e., what percentage of the speakers’ talk could they understand) andrate overall impressions. As students in each class are grouped according to their proficiency, we
believe that listeners might be able to provide some useful feedback for speakers on their mid-clausepausing and overall comprehensibility. Below are screenshots of the application login page,
homepage, and congrats message.
After students log in screen, the home screen is where students record their speech rate (SeeFigure 1 above). The green star above the speedometer on the second and third smartphone screensfrom left represent students’ fluency goal. The speedometers have the features of average syllables
per minute, maximum speed, total number of syllables, and total number of fillers. The design is
inspired by Salomatin’s (2019) speech rate screen, but has the addition of a timer, fluency goal, and165

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Figure 1
Application Login, Homepage, and Congrats Message

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and Kerismakersyllable counter. If students receive a speech rate at or above their goal, then the “Congrats” message
appears on the fourth screen. The speech rate feature shown in the speedometer is untrimmed whichmeans that disfluencies such as “um” and “uh” have not deleted. Other features include a) a noise
suppression filter to reduce background noise so that multiple students can perform the activity at
the same time, b) linking students’ accounts to Rikkyo University Gmail accounts for safety purposesand ease of access, and c) programming the application for both Android and iOS so that it isaccessible to all students with mobile devices. Figure 2 below shows the screens for menu and thestatistics.
Figure 2
Application Menu and Stats

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and Kerismaker

The menu screen is the first image on the left of Figure 2. The menu items include home, stats,classmates, journal, record in Japanese, information, mic check, profile, and logout. When students
tap on “Stats,” the second screen from the left appears, and students can scroll down to view different
graphs (See Figure 2 above). These bar graphs include speech rate, peer feedback, mid-clausepauses, syllable count, self-assessment, and fillers. Figure 3 below shows screens for the listenerresponsibilities.

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Figure 3
Peer-Assessment Features

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and KerismakerOn the first screen from the left, there is a list of the students’ classmates. Before the 3/2/1
activity, students select their speaking partners. Once they select their partner, they can see thesecond screen to the left. Listeners press the green plus sign when they hear a mid-clause pause fromtheir speaking partners. If listeners make a mistake, they can press the negative (orange circle) toerase the pause that they mistakenly added to their partners’ data. Students need to be taught todetect mid-clause pauses, but we believe that teaching students to be sensitive to mid-clause pausing
is feasible given research findings of de Jong (2018) and Kahng (2018). After, listeners can rate theirspeaking partners on comprehensibility. They can slide the green circle to the percentage that they
can comprehend of the speaker’s speech. On the last screen from the left, listeners answer thequestions about the speakers’ performance. The more carrots they select indicates greater agreementwith the statements. Figure 4 below shows the journal entry sections for self-assessment andinstructors’ feedback.
Figure 4
Journal, Self-assessment, and Instructor Feedback Pages

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and KerismakerOn the first screen to the left of Figure 4, students review previous journal entries or select newjournal entries. When they select a date (e.g., April 11), they see the adjacent screen on the right withmetalinguistic questions to reflect on their performance. When they scroll down the journal section,

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there is a self-assessment screen. There are three self-reflection statements: a) I spoke at a natural
speed for me; b) I paused when I wanted to pause; c) I focused more on speaking quickly thanspeaking accurately. Selecting more carrots equates to a greater agreement with the corresponding
statements a)–c). We have decided to use these types of reflective activities so that there is a place
for students to discuss other mediating factors such as emotions, topic familiarity, or socio-pragmaticconsiderations affecting their performance. Figure 5 below shows the L1 mid-clause pause fluency,
L1 speech rate, and information pages.
Figure 5
L1 Fluency and Information

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and KerismakerOn the first screen from the left, students can determine their L1 speech rate. Shrosbree (2020)states that ideally Japanese speech rate is calculated by counting morae for long, spontaneous
speeches in part because Japanese is a mora-timed language rather than a syllable-timed one such as
English. However, as the application directly compares speech rates, it needs to use the same syllable
units. L1 pauses and L1 speech rate data can be used for stats graphs and speedometer as a baseline.
For goals, we imagine students continually update their goals to be a little faster than their previous
speech rate and they can use the information from their L1 speaking performance to set realistic
goals that are personalized. The third screen is the information page, which can help students
increase their metalinguistic awareness of spoken fluency. When they tap one of the questions,answers appear. Figure 6 below show the profile, fluency goal, and logout pages.
On the first screen, there is the students’ profile page with the language goal. They can change
their name, icon, language, and fluency goal. On the second and third screens, students can choose
their fluency goal by moving the green circle or the green star. The orange circle and star represent
goal recommendations based on students’ progress. The last screen is the logout page.
Another aspect is the application design itself. Rego (2015) stresses the importance of anaesthetically pleasing and accessible user interface. Memon (2019) reinforces this idea of designing
an application that meets the user’s needs, contains consistent elements throughout, and is
uncluttered. Although there is some technical terminology that might make the interface less user
friendly, we include a language option if students want to use the application in Japanese and we also
have a feature whereby students can double tap any technical words to retrieve definition andtranslation so that they can understand the technical words more easily. For instance, if they double
tap on the words “Syllable Count,” a definition appears. In the next section, we discuss possible
limitations of the application.

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Figure 6
Profile, Fluency Goal, Logout Pages

Note. Designed by primary author using Canva Pro with images from Red-Hawk, Jaruka, and Kerismaker

Limitations
Monologic and Dialogic Fluency
Studies have investigated differences between dialogic and monologic fluency. Monologic
speaking tasks include Maurice’s 4/3/2 and presentations whereas dialogic speaking tasks equate to
discussions with another partner. By extension, dialogic speaking tasks share more characteristics incommon with group discussions than monologic speaking tasks (e.g., turn-taking, and greater online
planning time). Researchers find that students’ spoken fluency increases significantly more for
dialogic speaking tasks than for monologic speaking tasks (Ferrari, 2012; Michel, 2011; Michel et al.,2007; Tavakoli, 2016); During dialogic speaking tasks, students produce less disfluencies of filled
pauses, replacements, reformulations, and repetitions (Michel, 2011; Michel et al., 2007), have lesspauses and hesitations (Ferrari, 2012); and have shorter length of pauses, faster articulation rates,
longer fluent runs, and higher phonation time ratio (Tavakoli, 2016) than monologic speaking tasks.
Researchers cite Pickering and Garrod’s (2004) interactive alignment hypothesis, according towhich, interlocutors imitate others’ spoken production. They create semi-fixed expressions or
routines and reduce cognitive demand of making decisions by streamlining language comprehension
and production. Compared with dialogues, monologues have increased cognitive demands for
spoken production with no partners to rely on (e.g., less time for online planning because listeners’
attention wanes) (Tavakoli & Foster, 2008). The limitation is that applications cannot differentiate
speakers’ voices during discussions so even though dialogic fluency is more relevant to discussion,
we can only assess monologic fluency. Even with differentiation, determining who owns the pauses
during dialogic speech is challenging. As EDC only assesses spoken fluency during group
discussions, there is a limitation insofar as the mobile application measures only monologic fluency
and researchers have shown that students tend to be less fluent for monologic fluency than for
dialogic fluency.

Highly Proficient Students
Speed fluency increases with L2 proficiency, but a ceiling effect exists around levels B2 and C1

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of CEFR whereby students’ speed usually stays constant (Tavakoli et al., 2020). Additionally, fluency
judgments no longer become a determining factor of L2 speaking performance assessment for levels
C1 and above as raters consider other aspects such as speakers’ accuracy or complexity (Tavakoli &Wright, 2020). Therefore, upper EDC Level 1 students might not increase their speed fluency andeven if they do increase it, it might not be a determining factor for overall L2 speaking performance
ratings. From our own observations, Level 1 students’ pausing behavior seems natural rather than aresult of possible L2 disfluencies. Building spoken fluency and using a mobile application to track
fluency might not be as justifiable for these students; however, other course goals are still importantsuch as building confidence in their ability to use English for communication, to better express theirideas, and respect others’ opinions.

Characteristics of Fluency Development
Larsen-Freeman (2020) stresses the importance of acknowledging that language and language
learning is complex and dynamic. Spoken fluency, one aspect of language, is also characterized bythese attributes as its development is dynamic and nonlinear with complex factors such as individual
differences impacting it. For example, influencing factors can be internal such as motivation,
personality, language learning aptitude, and L1 speech rate whereas external factors can bebackground noise from other students or ease or familiarity with topics, as well as the speaker’s
interlocutor (Tavakoli & Wright, 2020). There are many factors that affect students’ fluency, andbecause of this, students might not always see increases in speed and decreases in pause frequency.
Although strong positive correlation exists between objective measurements of L2 overall proficiency
and perceived fluency (Bosker et al., 2012; Derwing et al., 2004), EDC is only for one semester (i.e.,four months); thus, raising students’ awareness of these internal and external factors, and theirability to affect students’ spoken fluency, is important because students might not understand why
working hard does not always translate into increases in spoken fluency.

Research Project
The application design was preceded by an ongoing study using an existing application,
SpeechRate by Yuri Salomatin (2019), to record and measure the words spoken per minute by
students during 3/2/1. We chose this application after comparing applications and software;
SpeechRate was the most accurate for calculating the words spoken per minute while also having
benefits of being accessible offline and free on iOS and Android. Participants were 20 first-year
university students at Rikkyo University, and they recorded each round of their 3/2/1 fluency activity
in the beginning of the lesson. SpeechRate did not store data, so students had to enter word counts
on Google Sheet. To account for the additional time to conduct this activity, 3/2/1 had to be reduced
to 2/1.5/1. After the activity, they could write notes about their performance in English or Japanese.
At the end of the semester, students were asked to fill out a survey discussing their perceptions ofusing technology to assess their fluency. From our experience, participants asked more questions
about developing spoken fluency and stated that they prefer having specific spoken fluency goals.

Conclusion
EDC course designers write that spoken fluency is the most important language-based objective

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for the class (Hurling, 2012). Currently, assessing spoken fluency equates to instructors’ perceived
quickness of students’ academic discussion skill use during group discussion performance. Having
an application that can monitor students’ monologic spoken fluency progress might help students
and instructors to better achieve EDC’s language-based objective of improving spoken fluency byincreasing metalinguistic awareness and providing feedback on performances. Applied linguists
characterize spoken fluency as complex and dynamic insofar as it develops nonlinearly, is multidimensional, and has a multitude of factors that influence it such as students’ L2 proficiency, L1
speech rate, and topic familiarity. Fluency is also considered to be the most difficult aspect of L2
speaking performance to assess according to Kang et al. (2019), but using mobile applications totrack fluency might help. There is more work that needs to be done in designing, developing, andimplementing the application, but we look forward to the process and value the feedback that we
receive from students and other interested parties.

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