Motivating Japanese University EFL Learners to Produce Longer
Speaking Turns with Web Based Machine Translation: A Pilot Study
Andy Rushton
Abstract
Learner use of web-based Machine Translation (WBMT) is now the ubiquitous realityof second language courses. As AI deep learning technology continues to underpin theaccuracy of such online tools, it is now becoming apparent in the literature that rather thantry to prohibit the unavoidable use of such devices it is worthwhile for educators to embrace
their affective motivational languagelearning potential. The forced shift to online lessons
during the 2020 COVID-19 pandemic resulted in Japanese students at the university inquestion engaging in L2 English-speaking practice in an asynchronous online environment.
As a way of preparing for tasks, students had autonomously relied on Google Translate (GT)to prepare monologic responses to speaking tasks. Although the results were at times
lexically out of context, learners had been empowered to participate through the technology.
The coincidental discovery and subsequent application of the highly accurate WBMT DeepL
meant that learners were able to produce longer, more accurate, and therefore moremeaningful L2 utterances. A pilot study was carried out which indicated that, as a result of
the positive experience with WBMT, the usually-reticent lower-level learners were more
motivated to participate in L2 speaking tasks. Furthermore, a large percentage of learners
felt that due to increased exposure to the L2, WBMT was a language learning tool rather
than simply a means to an end (cheating). It is proposed that a fuller investigation isconducted with the sole-use of DeepL to provide language prompts rather than complete
sentences.
Introduction
In April 2020, Japanese university EFL lessons shifted online due to the COVID-19
pandemic. In the university in question, the asynchronous platform FLIPGRID had beenchosen as a way to manage online oral communication lessons due to the longer time itafforded lower-level learners to complete speaking tasks (Rushton, 2021[1]). It had become
necessary to provide bilingual speaking task instructions for these learners and the webbased machine translator (WBMT) DeepL, with its AI neural networks, had been
implemented to achieve this. The accuracy of DeepL meant that precise speaking task
instructions in L1 (Japanese) could be quickly and easily conveyed to students. This
researcher’s experimentation with DeepL in professional and personal communication
proved that it was not only a highly accurate and natural translator but also a valid
language learning tool. Students had already been open about using Google Translate (GT)to complete asynchronous speaking task preparation. In the sudden online environment, it
had been impossible to monitor and therefore unavoidable in the circumstances. The
speaking task results, although longer than usual face-to-face classroom interactions,contained WBMT generated English that was at times lexically not very natural or justplain inaccurate. After the extremely positive experience regarding accuracy of thisresearcher’s use of the more accurate WBMT DeepL, it was decided to try and implement it
as a language learning tool in oral communication classes, from the Fall semester of 2020and on into and the end of Spring 2021.
This short paper will examine the results of a pilot study into the attitudes andopinions of Japanese university-age learners on the use of WBMT, namely DeepL, on their
preparation and production of long-turn speaking tasks. A brief review of the literaturesurrounding WBMT will be followed by the results of a short questionnaire given to three
university oral communication classes.
The Conflicting Views Surrounding WBMT
There are two fundamentally conflicting stances regarding the use of web-based
machine translation (WBMT) for language learning, (Briggs, 2018[2]; Case, 2015[3]; Clifford,
et al, 2013[4]; DeWaelsche, [5] 2015; Ducar et al, [6] 2018; and Stannard, [7] 2018). The first
position, and unsurprisingly the one endorsed by many educators, believes that the use of
WBMT is problematic because: it is an obstacle to language learning; promotes laziness; andultimately, at the university level, constitutes cheating. Furthermore, there exists thepotential threat to language learning programs i.e. teaching jobs. The second viewpoint sees

－ 73 －WBMT as being a language learning tool influencing learners’ L2 engagement at both the
linguistic and affective levels. Furthermore, it predicts that there will, in the future, becomplete acceptance of WBMT as a language learning tool by the educational establishment.
Against the Use of WBMTThe taboo surrounding the use of WBMT exists mainly because it is viewed as
cheating by both teachers and students (Case, [3] 2015). Many university courses have an“Honor Code” that clearly prohibits WBMT for use on graded coursework. However, students
under the usual time pressures of participating in a second language course, turn to WBMT
to provide a quick copy-paste fix. This L2, according to teachers, is rarely analyzed or
acquired by the learner. Also, it is argued that learners do not have the necessary language
awareness in the L2 to correctly monitor their own output when it is wrong, or learn from it
when correct, (Clifford, et al, [4] 2013). In this case, output refers to L2 produced by WBMT
which at the time of writing would, if not directed by teachers otherwise, primarily rely on
learners' sole use of the application Google Translate. Ducar et al, [6] (2018) refer to GT as
the “inescapable reality” of the second language (L2) classroom. Certainly, from anecdotal L2
learning experiences, GT does produce a mixed range of translations in terms of quality
ranging from the acceptable to the contextually inappropriate and at worst the absurd. Can
nothing good come from WBMT? Is it only to be relegated to the realm of cheating?
Stannard, [7] (2018) states that while he understands the viewpoint of educators being
averse to translation in L2 learning, he has changed his stance on GT from the perspectiveof being an L2 learner of Polish.
In Favor of the Use of WBMTIt is fruitless, according to Clifford, et al, [4] (2013) pretending that the “inescapable
reality” of WBMT does not exist because it is generally acknowledged that practically all
students possess a smartphone. Add in the acknowledged potential of WBMT as a language
learning tool by educators themselves, (Stannard, [7] 2018), and it is perhaps necessary toenquire as to what other reasons the inclusion of WBMT in the arsenal of already available
language learning tools, should be considered. Raising language awareness, developing
intercultural competence and learner autonomy, along with increasing electronic literacy
and critical thinking ability, according to Ducar et al, [6] (2018) are all possibilities. Clifford,
et al, [4] (2013) see “great potential” in WBMT assisting learning by providing language in
two languages (L1 and L2) which highlights a focus on form and language awareness. Case,[3] (2015) acknowledges that students are guaranteed to utilize it and consequently suggeststhat teachers, supplying the appropriate tasks, teach skills to assist students to assessmachine translation. Briggs, [2] (2018), concurs that training students to use WBMT
constructively and critically to benefit their L2 progression is the way forward. Such tasks
might fall into two categories: post-editing and contrastive analysis. Post-editing requires
the translation of a text into the L2 using WBMT followed by the learner locating, analyzing,
and reformulating any errors; and contrastive analysis utilizes student-generated L2
translated with WBMT into the student’s native language to highlight both grammaticaland lexical errors.
Motivation and classroom dynamics
Communication apprehension resulting in silence among Japanese University EFL
students due to their fear of making mistakes in public is a reality of both L1 and L2
interactions, (McGroskey, et al, [8] 1985). The perfectionist attitude of Japanese EFL
learners with regard to L2 grammatical accuracy exemplifies this (Rushton, [9] 2019).
Following extensive research into the concept of “expectancy of success” in motivational
psychology, it is now a recognized fact that we do better if we believe we can succeed(Dörnyei, Z. [10] 2001). As a teacher, it is, therefore, possible to set task conditions to bringabout individual positive learner expectancy. Such a positive condition could be the inclusionof WBMT in language learning activities. Garcia and Pena [11] (2011) found that beginnerand low-intermediate learners of Spanish had increased levels of satisfaction when
producing L2 texts with WBMT. It helped learners write longer texts and initiated greaterinteraction with the L2. WBMT acted as a scaffold enabling learners to write more authentic
and meaningful texts in the relaxed learning environment harnessing technology as the“facilitator and stimulator of communication.” Briggs, [2](2018) stresses that it is importantthat teachers do not regard WBMT as a shortcut that damages the language learning－ 74 －

process. To be able to produce language in L2 via WBMT, which is beyond the current abilityof the learner, is a potentially motivating force. Students are able to produce meaningful L2
which reflects what they want to really communicate in a way that exposes them tolanguage that they previously did not know. Case, [3] (2015) concurs, stating that WBMT
allows students to conceptualize language as a communication tool rather than justmeaningless lexis.
Equality
As well as the aforementioned cultural reticence in communicative language learning
contexts, there is also a discrepancy between the engagement in classroom tasks of students
from privileged backgrounds and those from disadvantaged backgrounds. Ideally, students
should be assigned to classes populated by learners from similar learning backgrounds andlinguistic abilities enabling them to engage in level-appropriate content (Vygotsky, 1978).
However, in a university environment, students from less privileged backgrounds with fewerprior communicative learning opportunities may quickly become demotivated by the sheer
overwhelming nature of the need to produce L2 either orally or in writing. This can result in
nonparticipation or at best minimal interaction. By means of WBMT, the latter group can atleast have a chance of keeping up in a competitive learning environment such as auniversity EFL program. Furthermore, they could be motivated through initiating their own
L2 utterances or texts and learn from analyzing their own production and teacher-directed
negative feedback on attempted L2 utterances. (DeWaelsche, [5] 2015).
The Changing Face of Education
WBMT can upset traditional language learning approaches (Case, [3] 2015). As
educators, language program policies need to be “proactive and forward-thinking” to develop
new effective learning environments (Clifford, et al. [4] 2013). Furthermore, teachers should
question whether their current methods of teaching are still viable with students whose
experience of the world is primarily viewed through online interactions and the numerous
implications they have for communication, and ultimately education.
WBMT has the potential to undermine traditional learning, with the old rules, e.g.
the use of WBMT as a form of cheating, being seen as an impediment to advanced new
methods and instruments. With WBMT, translation exercises become outmoded, giving riseto newer innovative exercises and activities. Using digital tools, it is possible more than everbefore to tap into authentic language opportunities existing outside a university context.
The Research Methodology
Overall Research Aims
To investigate the attitudes and opinions of Japanese University EFL learners on theeffect of WBMT on motivation towards the preparation and production of L2 longer speaking
turns in oral communication classes.
Research Questions
1. What are the overall attitudes and opinions of Japanese university EFL learners
towards using WBMT to improve their preparation for a longer speaking turn, in
both face-to-face and asynchronous online learning contexts, for oral communication
classes?
2. Do the learners view WBMT as useful as a learning tool or is it considered to becheating and of little or no pedagogical value?
3. What was the impact of WBMT on motivation to participate in the oral
communication class?
The Participants
A total of 36 students, in three small classes, participated in the compulsory two-year
Oral Communication program at a women’s university in Hyogo Prefecture, in the west ofJapan. The CEFR level range was from A1-B1. There were two second-year classes with 13
and 16 students respectively, and one third-year class, with 7 students. The classes had
begun in April 2021 and after two weeks (two lessons) moved online due to the COVID-19
pandemic. Lessons then resumed on campus from week 10 and continued until the end of
the course (Week 15). When online, the classes mixed both synchronous and asynchronousinteractions practicing listening, L2 conversations, and a long turn speaking task being－ 75 －

completed asynchronously via the video discussion platform, Flipgrid. During face-to-faceclasses in accordance with the adopted social distancing policy of the university, the lessons
continued to focus on listening practice and longer speaking turns. Speaking tasks werecompleted as before on Flipgrid although speaking practice was carried out by controlled
pairwork activities coordinated to maintain social distancing and reduce the risk of COVID19 infection.
The Speaking Task
The speaking task was modeled on an IELTS Part 2 (long turn) speaking test to meet
the asynchronous online lesson environment as outlined in Rushton, [1](2021). This had
initially been as a reaction to the impact of the 2020 COVID-19 pandemic in moving lessons
online and the decision to make the oral communication lesson speaking practice segment,
asynchronous. Asynchronicity was chosen to allow students more practice time thusachieving greater grammatical and lexical complexity. The video discussion platform
Flipgrid was used for students to upload their video recordings in response to the speaking
task. It was noted that many of these oral recordings were read from a prepared script andhad been obviously translated with WBMT. Anecdotally, this was shown to be Google
Translate (GT), although no explicit instruction had been given regarding its use in speaking
tasks. The result was that even the most reticent and unmotivated students had been able toparticipate and produce a fuller L2 oral response than they had previously in face-to-faceclasses. These utterances were at times lacking appropriate use of lexis due to theinaccuracy of WBMT (GT), and this issue was dealt with in feedback provided by theteacher.
Selecting a better WBMT - DeepLAs mentioned, the free WBMT service DeepL had been used with remarkableaccuracy. DeepL is a cutting-edge free WBMT that uses artificial intelligence (AI)techniques based on deep learning, a branch of AI that tries to replicate the learning modelsof the human brain. As DeepL is more technologically advanced than GT or other WBMTs,
its translations are very natural, as if produced by a human translator (Language Solutions[12]). When explaining speaking tasks to students, DeepL was explicitly recommended withlinks provided in the task materials supplied on Google Documents. Students were
specifically advised to translate with DeepL rather than other WBMTs due to the moreaccurate translations it produced. There were, however, no restrictions regarding the use ofother applications, and other WBMTs were certainly used (GT, LINE messaging translation,
Papago etc.). Despite this, DeepL was the most prevalent WBMT application used duringface-to-face classes.
Applying the WBMT
Students were required to speak upwards of 30 seconds to a maximum time limit ofapproximately two minutes with most learners averaging one minute. In the online
environment, a preparation/upload deadline was given for 23:59 on the day of the lesson. For
face-to-face lessons, this was the end of the 90-minute lesson with some exceptions when
uploading was hampered by poor internet connectivity. The aim was to produce sufficient
text on a specific topic to constitute a longer speaking turn. Students, therefore, translated
from L1 to L2 with WBMT sentence by sentence. They produced highly personalized anddetailed L2 as a result. In many instances, the L2 that was produced was unknown to themprior to engaging with the speaking task and hence provided an opportunity for L2 exposure.
Research design
At the end of the penultimate lesson, a six-question survey was administered on
Google Forms. Students accessed the survey by scanning the QR code and completing it on
their smartphones. Five questions were multiple choice and covered aspects of the researchquestions relating to DeepL being a learning tool vs cheating; its efficacy at producing
results to complete the task; and its effect on motivation towards participation in the oral
communication classes. The sixth question was an open prompt for respondents to sayanything else they wished to about DeepL and indicated that responses in L1 were
acceptable. A total of 26 students from the three classes participated in the survey.

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Results
Although 26 participants completed the questionnaire, one was excluded for checking
more than one response for each question. The results of the six questions are as follows:

1. How often did you use DeepL in your oral communication classes this semester? (Fig.1)

Out of the 25, 17 (68%) reported that they had used DeepL every lesson and one person (4%)
used DeepL 7-10 times. At any given point in the course, at least 68% of students were using
WBMT rising to a maximum of 88%. Using DeepL 3-6 times and 1-3 times both had two
responses each (8%), and Never was reported by three respondents (12%). It can be seen thatthe majority of students used DeepL on a regular basis. It should be noted that despite being
repeatedly advised to use DeepL due to its superior quality of translations, some students
ignored this recommendation and routinely used GT or the translation feature in LINE
messaging application on their smartphones. There were no firm directives in place to limit
their agency regarding the selection of which WBMT they used.

(Fig. 1)

2. Because of DeepL I could make my English more interesting. (Fig. 2)
12 (48%) claimed this was True; 11 (44%) Somewhat true; and 2 (8%) that it was False. The
vast majority of students acknowledged that the use of DeepL was able to make theirutterances more interesting to some degree.

(Fig. 2)

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3. I could learn new words and expressions because of DeepL. (Fig. 3)
18 (72%) stated that this was True; 5 (20%) Somewhat true and 2 (8%) that it was False.
Almost all the students said that they could “learn” new words and expressions. This should,
however, be interpreted solely as exposure to new lexis as no formal method was employed
to measure actual L2 acquisition of such items.

(Fig. 3)

4. What do you think about using DeepL in oral communication classes? Is it cheating or isit a learning tool? (Fig. 4)
24 (96%) said that it was a learning tool and one (4%) that it was cheating. Overwhelmingly,
all students, bar one, could recognize the benefits of using WBMT as a learning tool in theirstudy of the L2. This would seem to concur with the result of Question 3 above.

(Fig. 4)

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5. My motivation to participate in oral communication class increased because of DeepL.

(Fig. 5)

10 (40%) said it was True; 13 (52%) reported that it was somewhat true; and 2 (8%) that it
was False. Interestingly, 24 out of 26 students admitted to some measure of increased
motivation due to using the WBMT DeepL in their oral communication classes.

(Fig. 5)

6. If you have anything else you want to say about using DeepL in oral communication
classes, please write it below you may write in Japanese.
There were a total of three answers. Two were in English and agreed that DeepL was “agreat educational tool” Another, in Japanese, was translated as, “It was very easy to use as
it translated exactly what I wanted to say.”
Discussion, Implications, and Recommendations
The aim of this pilot study was to investigate the attitudes and opinions of Japanese
University EFL learners on the effect of WBMT on motivation towards the preparation andproduction of L2 longer speaking turns in oral communication classes. There were threeresearch questions (RQ) to consider.
RQ 1. What are the overall learners’ attitudes and opinions towards using WBMT to
improve their preparation for a longer speaking turn, in both face-to-face and asynchronous
online learning contexts, for oral communication classes?
There was a positive overall feeling from learners towards WBMT for the preparation for
longer turn speaking tasks in oral communication classes (Briggs, [2] 2018; Case, [3] 2015;
Clifford, et al, [4] 2013; DeWaelsche, [5] 2015; Ducar et al, [6] 2018). The majority ofstudents used WBMT in many if not all classes. Most learners felt their utterances weremore interesting. This was most likely due to the scaffolding provided by WBMT, allowing
them the freedom to be specific and detailed about what they wanted to say.
RQ 2. Do the learners view WBMT as useful as a learning tool or is it considered to becheating and of little or no pedagogical value?
All learners, except one, viewed WBMT as a learning tool rather than a means of cheating.
This is probably because students could discover a lot of new lexis and expressions tocomplete the speaking task. As a result, most of them felt their spoken output was also moreinteresting no doubt because of a greater range of vocabulary and expressions that were attheir disposal to compose longer and more interesting speaking turns (Briggs, [2] 2018;
Case, [3] 2015; Clifford, et al, [4] 2013; DeWaelsche, [5] 2015; Ducar et al, [6] 2018; andStannard, [7] 2018).

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RQ 3. What was the impact of WBMT on motivation to participate in the oral
communication class?
The majority of learners reported frequent use of WBMT in almost every class correlatingwith a similar amount who felt their motivation to participate had increased in oral
communication classes. This increase in motivation may well be attributable to theincreased “expectancy of success” (Dörnyei, Z. [10] 2001) students could expect through theuse of WBMT, notably DeepL when preparing their speaking tasks.
Conclusion
Summary of findings
It can be seen that WBMT, through its motivating effect, has an overall positive
influence on the participation of Japanese EFL learners in oral communication classes.
Furthermore, as students engaged in language production, discovering L2 lexis, lexical
chunks, expressions, and phrasing that were previously unknown to them, the pedagogicalvalue of WBMT as a potential learning tool cannot be ignored.
Limitations of the study and recommendations for further research
While an overall positive effect of WBMT on the motivational participatory aspect ofan oral communication class has been noted in the study, the quality and consistency of theoutput were not scientifically monitored. The disparity between GT and DeepL in terms ofaccuracy and quality of translation is not only noted on popular media (Language Solutions[13]) but anecdotally as a teacher listening to students’ speaking tasks and providing
feedback. GT, while satisfactory at the word level, usually provides only literal translations
which affect the accuracy, intended meaning, and appropriacy of the spoken text. In the
future, it would be more beneficial to be absolute regarding the choice of WBMT i.e. limiting
the students to DeepL only rather than the assumed default option of GT.
The L2 produced by learners in the pilot study was essentially just read aloud. It is
proposed that learners should be taught how to highlight key information words fromtranslated texts and practice producing longer speaking turns by using such notes asprompts.  .
Furthermore, language is more than just a grammatical or lexical entity, and thearea of pronunciation in this study was not focused on. It is suggested that in the future thearea of pronunciation should be more fully explored. This may be possible by reducing thelength of the speaking turn to make such a focus more manageable.
Finally, this pilot study focused on engaging generally lower motivated learners to
prepare and produce L2 English to satisfy a specific classroom speaking task. The long-term
effects on language acquisition are as yet untested and can currently only be acknowledged
anecdotally by WBMT users such as Stannard, [7] (2018), and this researcher who areinvested in the need to use the L2 for real communicative purposes and not as part of auniversity oral communication program.

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