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The Effectiveness of Robot Pronunciation Training for Second Language Acquisition by Children: Segmental and Suprasegmental Feature Analysis Approaches

Jong-Won Kim, Jung-Kwan Kim International Journal of Robots, Education and Art. Volume 1, Number 1, February 2011

The Effectiveness of Robot Pronunciation Training for Second Language Acquisition by Children: Segmental and Suprasegmental

Feature Analysis Approaches

Jong-Won Kim Department of ESL and General Education, Korea University, Seoul, Korea

{[email protected]} Jung-Kwan Kim

Technology Exploration Department, KT Central R&D Laboratory, Seoul, Korea {[email protected]}

doi:10.4156/ijrea.vol1. issue1.1.

Abstract This study introduced the effectiveness of robot pronunciation training for Korean children's

second language acquisition by investigating whether there would be significant differences between before- and after pronunciation training by robot, iRobiQ, as an instructional assistant about the Koreans' most problematic target segmental features, consonants and vowels, and suprasegmental features, intonation, to 18 Korean EFL students at Songnisan Sujung Elementary School. This current study used a mixed research design, quantitative and qualitative, for data collection and analysis. The overall findings from all 8 weeks' treatments yielded statistically significant results as overall learning outcomes after all treatments. While training target pronunciations with a robot, iRobiQ, most of the students showed their positive perceptions about robot iRobiQ as a learning assistant and familiar tool, together with their improved self-confidence and comfort levels in terms of showing more frequent contact with a robot, iRobiQ, and their positive views on robot as an educational tool. The overall results suggest a new paradigm about our knowledge and implications in teaching and learning English pronunciation for Korean EFL elementary school students in second language acquisition.

Keywords: Robots, Segmentals, Suprasegmentals

1. Introduction

Together with the brilliant and very fast development of technology, robots as the intensiveness of high-technology have been changing paradigm of various fields of humans’ life. In particular, various kinds of robots such as home-robots, silver-robots, pet-robots, and education-robots have been also invented and employed for a variety of purposes.

Recently, Korea has also developed and used many kinds of robots, especially, ones which have served as teaching and learning assistants and aids in the field of language education: iRobi, iRobiQ, MentoRo, U-Robo, Cubo, and Tiro [1]. In a study carried out to investigate the educational impact of using a robot in English education such as storytelling and pronunciation together with examining students’ perceptions, Kwak, Lee, Han, & Kim [2] revealed that using robots in English education helped improve learners' motivation and were more effective than other traditional approaches. Besides, most of the studies conducted to examine the educational and instructional effectiveness and roles of robots in such various countries as the USA, the UK, Japan, Canada, and Korea have found that children's learning with robots could be more supportive, effective, and successful in second or foreign language learning [3].

Most language teachers agree that pronunciation accuracy and comprehensibility are essential for successful communication. Similarly, most of the students see pronunciation as a vital part of their second language acquisition. Thus, both teachers and students have spent a lot of class time and hours for learning and teaching intelligible pronunciation [4].

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In spite of the recognized importance of pronunciation, many of the second language teachers still

stay uncertain how to teach pronunciation in the curriculum. In addition, most of the language courses have emphasized more general oral communication over pronunciation. To help pronunciation pedagogy in L2 classrooms, Korea has tested the use of English-teaching robots available to help children enhance English pronunciation in terms of being able to read aloud English stories or to develop widget program contents for the evaluation and correction of English pronunciation of Korean-speaking learners [5].

More importantly, with respect to two types of pronunciation features, segmentals and suprasegmentals, Korean-speaking learners have their own English mispronunciation patterns. According to Levis and Grant [4], some of the segmental features, consonants and vowels, and suprasegmental features such as stress, rhythm, and intonation can be especially problematic to Korean-speaking learners. Moreover, suprasegmental features are more important than segmental features in real and authentic communication because suprasegmentals are more clearly connected to functions of spoken English [4].

Thus, based on these theoretical and practical suggestions about English-teaching robots and English pronunciation in English education, this current study addressed the practical challenges related to robots and English pronunciation. Through this, this current study introduces the instructional effectiveness of Children's English pronunciation training by using robot iRobiQ developed by YuJin Robotics, Korea, and learners' perceptions of iRobiQ. Next, this study suggests some guides and implementation strategies needed for using robots in second language classrooms. 2. Review of the Literature 2.1. Humanoid-Robots in Second Language Acquisition

Very recently, a variety of robots have been competitively developed for various purposes. For example, in Korea, Yujin Robotics created 'iRobi' and 'iRobiQ', Hanool Robotics invented 'TIRO", HanulKid developed 'MentoRo', Dasa Robotics made 'Dooly Robot' and INNOmetal IZIrobot produced 'CUBO'. Similarly, in foreign domains, Honda developed 'ASIMO', Sony created 'QRIO', and Lego produced Lego MIldstorms. Some of these domestic and foreign robots have been used as teaching assistants and aids for educational purposes, especially for English education [6].

A few of the many studies conducted in the field of second language education in the domestic setting and foreign domains have used robots as teaching or tutoring assistants in order to investigate the pedagogical effectiveness of robots for second language education. In Korea, Lee et al. [6] investigated that using robots as pedagogical assistants in English education could be more effective, motivating, and supportive than any other approaches in elementary school classroom settings.

More specifically, in Korea, a robot named 'iRobiQ' invented and developed by Yujin Robotics, Korea, has been applied to r-learning for second language acquisition norms with demonstration to assist the teacher and monitor learning process together with or instead of the existing multimedia such as textbook with audio tape and e-learning [7]. The robot iRibiQ has been equipped with various kinds of ample educational programs and contents, esp., for early childhood education, spoken fairy tales, song and rhythmic movement, encyclopedia, and Korean and English studying. Thanks to these various kinds of educational contents of the robot, iRobiQ, some studies have been conducted with the robot, iRobiQ, and have reported that there were significant effects and improvement in using the robot iRobiQ. Thus, iRobiQ has been one of the important teaching assistants and tools changing paradigm of English education into r-learning from various pedagogical approaches [6], [8].

Korea has examined the use of robots for the teaching of second language, and the robots are to be placed in apartment complexes in which they can help children practice English pronunciation. The robots are said to be able to read aloud English stories and to correct children's English pronunciation [5]. In a study carried out to explore the feasibility of using robots as language instruction tutors for young children, the children in the robot-assisted groups improved significantly compared with media-assisted groups in linguistic ability such as story telling, understanding, word recognition, and PPVT (Peabody Picture Vocabulary Test) [8]. In addition, in one study conducted to investigate the

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effectiveness of r-learning services for elementary students' English teaching and learning, Hyun et al. [8] concluded that children had good personal relationship with robots in class, and that teachers also preferred the robots related to their convenience to manage the lesson together with enhancing motivation and interaction, and showing positive perceptions among the robot, teachers, and children. More importantly, in a study conducted to examine the effects of educational use of a home-robot, iRobi, as a home tutor for children in English education, Han et al. [9] pointed out that educational robots as a tutoring aid in children's English education can help children improve their concentration, motivation, and educational proficiency and competency. 2.2. English Pronunciation Problems of Korean Speakers

Most English teachers recognize very well how much pronunciation is important. On the other hand, it may be sometimes true that little enough attention has been paid to the students' pronunciation in the process of second language instruction [5]. According to Ma [10], non-native speaking speakers can have what extent their own difficulty and problems of second and foreign language pronunciation, and, partly, there can be a little difference in difficulties and problems of pronunciation of segmental and suprasegmental features.

Similarly, Koreans have their own challenges in English pronunciation. According to Avery and Ehrlich [11], the English pronunciation problems of Korean speakers can be quite severe because of the significant differences between the sound systems of Korean and English. For example, Korean does not have the sounds /f/ and /v/ of English, and Korean speakers tend to substitute /p/ and /b/, respectively. Also, Korean has no voiced fricatives, and Korean learners tend to substitute voiceless stops or affricates for English voiced fricatives. Particularly troublesome is the English /z/ sound in words such as 'zone' and 'zoo'. Korean learners generally pronounce this /z/ sound as /dz/ or /ts/. In addition, Korean has aspirated voiceless stops and unaspirated voiceless stops but no voiced stops. Thus, Korean learners may have difficulty in perceiving and producing the difference between voiced and voiceless stops in non-initial position. Additionally, in Korean, /s/ is pronounced as either /∫/ (before high and mid front vowels) or as aspirated /s/ in most other positions. Therefore, words such as ‘seat’ and ‘sheet’ may sound the same (like ‘sheet’). Furthermore, words in which learners substitute their aspirated /s/ will sound quite odd to the English ear. Korean students tend to substitute /l/ for /r/ in initial position producing ‘light’ instead of ‘right’. Alternatively, they may substitute what sounds like a /r/ or a flap /D/ for /l/ between vowels producing ‘firing’ or ‘fighting’ for ‘filing’. Korean speakers will usually substitute aspirated /t/ for /θ/ and unaspirated /t/ for /ð/ [11].

Table 1. Segmental Feature Pronunciation Problems of Korean Speakers: Consonants and Vowels

Segmental Feature Pronunciation Problems of Korean Speakers: Consonants and Vowels

Problems Consonants Example Words Vowels Example Words Problem 1 /p/ vs. /f/ pan vs. fan /iy/ vs. /I/ sheep vs. ship Problem 2 /b/ vs. /v/ boat vs. vote /ey/ vs. /ε/ train vs. yes Problem 3 /v/ vs. /ð/ van vs. feather /uw/ vs. /ow/ boot vs. book Problem 4 /z/ vs. /ζ/ zoo vs. television /∧/ vs. /a/ cup vs. father Problem 5 /f/ vs. /v/ fan vs. van /ε/vs. /æ/ yes vs. hat Problem 6 /s/ vs. /z/ sun vs. zoo

Problem 7 /p/ vs. /b/ ripping vs. ribbing Problem 8 /k/ vs. /g/ lacking vs. lagging Problem 9 /s/ vs. /∫/ seen vs. sheen

Problem 10 /l/ vs. /r/ long vs. wrong Problem 11 / e/ vs. /ð/ Think vs. that

Furthermore, there is a "widespread consensus about the significance of intonation for successful

communication" [12] because an intonation system is an integral part of the language and inaccurate speech melody can trigger a complete breakdown in communication [13], [14], [15], [16], [17].

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Koreans have pronunciation difficulty with suprasegmental features such as stress, rhythm, and

intonation patterns of English. More specifically, Korean learners may have challenges with the characteristic intonation patterns of English because of Korean different sound pitch. Due to the significance of intonation, intonation is a fairly complicated subject, and intonation is considered to be a problem child of pronunciation teaching [12]. Thus, students need to have to practice all the characteristic intonation patterns of English: final rising as used in yes/no questions; final-rising as used in statements, commands and wh-questions; non-final rising-falling as used in complex sentences; and non-final rising as used in lists [11].

Table 2. Suprasegmental Problems of Korean Speakers: Intonation

Suprasegmental Problems of Korean Speakers

Intonation Patterns Example Sentences Intonation Rules

P1: Questions expecting a 'yes/no' answer

Did he fail the test again? Final rising(↗)

P2. Statements My brother is coming on Friday. Final rising-falling(↘) P3. Information questions What are you looking for? Final rising-falling(↘) P4. Imperatives Call me tonight around seven. Final rising-falling(↘) P5. Question tags (expecting confirmation)

You like orange juice, don't you? Final falling(↘)

P6. Question tags (showing less certainty)

Your train leaves at six, doesn't it? Final rising( ↗)

P7. Lists of items We went to London, Paris, Cairo, and Boston.

rising, rising, and final falling (↘)

P8. Surprise A: Mr. Gray takes the train to Plainview at 8:18 every day. B: To Plainview?

Final rising(↗)

2.3. Input and Output Method of Robots

Humans' voice sounds are the most natural method of interaction and communication input and

output. Historically, however, for some specific purposes of voice and sounds, many kinds of technological machine voice and sound have been invented and developed by technology for the use of voice and sound input and output. Despite the development of this machine voice and sounds, they are still not enough for high qualified human-like interaction and communication. Thus, due to the limitation of technology, technological voice and sounds have had many constraints and limitations of interaction and communication [18].

On the other hand, very recently, the quality of the technology of the sounds and voice input and output has been advanced more in segmental and suprasegmental features that are able to teach both isolated and linked word format, discrete word recognition, and continuous speech recognition, making speech/sound a viable means of input and output [19], [20], [18].

In using robots as instructional assistants, voice and sound input and output can be the most important for interaction and communication between students and robots. Recently, many companies have started developing educational robots for language teaching, which suggests that a more natural voice and sound will be developed with correct pronunciation incorporated [5]. Thanks to advanced r-learning technology, voice and sound input and output methods have been also developed. For example, first, gesture-based interaction and communication approaches related to gestures and eye/head movement can be one useful input and output method and capability for non-verbal interaction and communication between students and robots. Some good examples would be: 'finger pointing', 'eye contact' and 'head movement' [18]. Consequently, the ability to recognize these kinds of gestures during the interaction and communication in robot learning system can be very important. Second, keyboard, mouse/joystick, and screen (monitor) can be other important input and output ways for interaction and communication between students and robots [18]. Through these various methods

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of input and output from robots, interaction and communication between students and robots have been improved.

3. Methodology

3.1. Research Questions

To investigate the effectiveness of robot pronunciation training and what the Korean EFL

children's perceptions of robot are, the QUAN-Qual Model [21] was used, and one quantitative and two qualitative research questions in this study were suggested as follows: 1) Are there any significant differences among overall learning outcomes of a robot iRobiQ pronunciation training

for the acquisition of English segmental and suprasegmental feature pronunciation by Korean EFL elementary

school students? 2) Do Korean EFL elementary school students perceive a robot iRobiQ, pronunciation training approaches as

leading to more learning than traditional pronunciation instruction activities? 3) What are Korean EFL elementary school students’ perceptions of using a robot iRobiQ as a learning assistant

for second language acquisition? 3.2. Subjects

The subject pool for the study consisted of 18 native Korean elementary school students at

SongniSan Sujung Elementary School, Korea. Two of the classes at this school were chosen for the current study: the 4th grade (N = 7), and the 5th grade (N = 11). All subjects received the same amount of treatments, robot pronunciation training, from the same robot iRobiQ during 8 weeks. All subjects had similar metalinguistic and metacognitive knowledge about the target pronunciation as shown in the result of pretest. However, because this present study investigated the effects of the target pronunciation by the robot iRobiQ students who received more 80% in pretest were eliminated from the data because they already had considerable metalinguistic and metacognitive knowledge in the target pronunciation, so these treatments would not influence them. The subjects were mixed up with female and male students because this school classes are mixed-gender. Also, all students had the same sociocultural background of Korean because of their Korean heritage and their ages ranged from 12 through 13 years old. 3.3. Materials

In this study, two types of key material packets were designed to examine the students' overall

treatment outcomes of a robot iRobiQ’s segmental and suprasegmental pronunciation training and the students' perceptions about using robot iRobiQ: test materials and survey questionnaires.

3.3.1. Robot iRobiQ

This current study used a robot iRobiQ as an instructional tutor. iRobiQ is a small, intelligent robot

invented by Yujin Robotics Co., Ltd, Korea. Also, like human beings, iRobiQ can move its head, arms, and wheels, and express its response and emotions in terms of using face lamps. iRobiQ's specification is as follows: Dimension (450 x 320 x 320 mm), Weight (7 kg), PC specification (Internal computer: Celeron 733 Mhz, 256 MB, HDD 40 GB), Display (7" (800 * 480)/TV Out), Audio (Stereo 2ch Speaker, Stereo Mic.), Battery (Charge time (charging duration) 3 hours, Use time (using duration) 3 hours), Power consumption (45 Watt), Maximum moving speed (50cm/sec). Moreover, iRobiQ has various kinds of linguistic interaction types as follows: guided repeated oral

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reading, reading aloud, paired reading, shared reading, simple question, interactive question, simple feedback, interactive feedback, read presented picturebook, simple activity for story, making/understanding save portfolio, and segmental and suprasegmental pronunciation training contents and evaluation, and oral speaking practice texts [8].

Figure 1. iRobiQ and its Screen-Shot Pronunciation Contents

3.3.2. iRobiQ's Contents

For segmental and suprasegmental pronunciation training by the robot iRobiQ, iRobiQ's r-learning

contents, "TouchMon" and "TouchEnglish" designed by KT Central R&D Laboratory for English pronunciation error correction demonstration, were used. "TouchMon" consists of 35 sentences for suprasegmental feature pronunciation (intonation) training, and "TouchEnglish", a kind of word game, was used for segmental feature pronunciation (consonants and vowels) training during the 8 weeks of the period of time for treatments.

These iRobiQ's linguistic contents have been designed and upgraded by the professional staff of KT. Based on these advancement and improvement of iRobiQ's educational contents, r-learning using iRobiQ as the instructional assistant robot has been served and developed as an important educational media in various fields of education. These contents consist of four steps: S1, S2, S3, and S4. The first step, S1, is the first screen consisting of 'INTRODUCTION', and the second step, S2, comprises to 'TouchMom' consisting of three levels, 'Level I', 'Level II', and ‘Level III’. In the second step, students can choose each level and one of the three levels for suprasegmental feature practice. The third step, S3, consists of ‘TouchEnglish’, made up of 'Level I', 'Level II', and ‘Level III’. The last step is 'Self-Evaluation' part, in which students can assess their segmental and suprasegmental proficiency.

Figure 2. iRobiQ's Programs and Contents

__________________________________________________________________________________________________________________________________________________________________ S1: INTRODUCTION S2: TouchMom: Suprasegmentals: Sentence Pronunciation Practice: Intonation

LEVEL I: Beginning Level LEVEL II: Intermediate Level LEVEL III: Advanced Level

S3: TouchEnglish: Segmentals: Word Pronunciation Practice: Vowels and Consonants LEVEL I: Beginning Level

LEVEL II: Intermediate Level LEVEL III: Advanced Level S4: Self-Evaluation

__________________________________________________________________________________

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3.3.3. Test of Segmentals and Suprasegmentals

The test material packets for the two tests, pretest and posttest, were designed. One was designed with segmental features, 8 consonants and 6 vowels which are the most problematic to Korean speakers, and another was designed with suprasegmental features, 8 intonation patterns in which Korean speakers can have challenges, respectively. However, all test packets included the same target segmental and suprasegmental features for the same proficiency level and training from a robot iRobiQ.

More specifically, the most problematic English segmental features, consonants and vowels, of Korean speakers [11] were selected as follows: 8 consonants: /p/, /f/, /b/, /v/, /s/, /∫/, /l/, and /r/, and 6 vowels: /iy/, /I/, /ε/, /ey/, /uw/, and /∧/.

The sentences under study were based on iRobiQ's suprasegmental features. 30 sentences showing 6 types of intonation patterns were selected for evaluating the students' mastery of intonation: (1) information questions (e.g., Where do you live?); (2) questions (e.g., Have you got a pen?); (3) statements (e.g., He lives in the house on the corner); (4) imperatives (e.g., Put it on the table); (5) question tags (expecting confirmation, e.g., You're French, aren't you?); and (6) question tags (showing less certainty, e.g., You're French, aren't you?). The test question items consisted of six sets of intonation patterns, and five sentences for each type of intonation pattern were designed based on iRobiQ's suprasegmentals.

4.1. Data Collection 4.1.1. Quantitative and Qualitative Data Collection

The robot iRobiQ was placed inside and outside classrooms in the target elementary school for the

students to be able to be exposed to the robot iRobiQ during eight weeks. In addition, all subjects were recommended to often contact iRobiQ individually. All subjects were also asked to learn and practice iRobiQ's contents including segmentals, consonants and vowels, and suprasegmental features, esp. intonation. Then, the researcher asked all subjects to sign the visiting lists up, and the researcher recorded and counted how often subjects contacted iRobiQ. The same procedures had continued for eight weeks.

The main purpose of this study was to investigate the effectiveness of the robot iRobiQ’s pronunciation training and the students' perceptions about iRobiQ between before- and after pronunciation training as a result of robot iRobiQ. To achieve this aim, the "Before-After-Design" or "Pre-Post-Design" [22] were administrated as procedures to the subjects by two different tests: one pretest and one posttest.

Based on the before-after-design, two different tests were administered by the same teacher, the researcher to the same students in the subjects' regular class hours at two different points before and after the experimental treatment. Both tests consisted of both segmental and suprasegmental features. The segmental test was for assessing the word segments (N = 98), consonants and vowels, of the target pronunciation, and the suprasegmental test was for evaluating the sentence segments (N = 30), intonation patterns.

The pretest in all subjects provided this current study with the guidelines to be able to definitely test how much significant difference in subjects occurred as a result of the overall treatments. Also, the pretest administered to all subjects before the experimental treatment was able to exactly investigate metalinguistic and metacognitive knowledge about the target pronunciation, segmentals and suprasegmentals, before the treatments began. The pretest was carried out by the same teacher, the researcher, one day before beginning the treatments from robot iRobiQ.

In addition, the posttest was available for evaluating whether there would be a significant difference between before- and after training as a result of the robot iRobiQ’s pronunciation training and for examining the scores gained in all subjects. The immediate posttest was conducted by the same teacher, the researcher in the same day as the final treatment, Week 8. Thus, in this current study,

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before-after-design procedures were employed instead of the after-only design. Table 4 shows the before-after-design treatments and procedures in this present study.

Figure 3. Before-After-Design Procedures

Before--------------------------------------------------------------------Treatments ---------------------------------------------------------After Week 1 --------------------------------------------------------------Week 1 to 8 -------------------------------------------------------- Week 8 Pretest ------------------------------------------------------------iRobiQ Treatments---------------------------------------------------One day

inside and outside classrooms Immediate test after eight weeks

In this study, survey questionnaires were designed to investigate the students' perceptions of robot,

iRobiQ, (N = 9) and its word-segment and sentence-segment pronunciation training (N = 8). To achieve this aim, questionnaires were administered with two types of questions: fixed-choice measures as quantitative and open-ended survey questions as qualitative.

The fixed-choice measures were followed by 5-point Likert scales to indicate agreement or disagreement. Subjects were asked to read each statement in Korean version and indicate their reaction by choosing a number from 1 = strongly disagree to 5 = strongly agree. These survey questionnaires were invented by the researcher. The version of the questionnaires was written in Korean in order to help students clearly understand the information.

In addition, open-ended survey question items were constructed for the subjects to examine the students’ perceptions about robot and its pronunciation training. In these questions, the subjects first read these open-ended survey question items and were asked to briefly write about what they personally felt and thought about the question items. Students were also given an opportunity to express their feelings and impressions about the treatments conducted in the study. Students were also permitted by the researcher to write all statements in Korean because they would have some difficulties expressing their opinions in English.

4.2. Data Analysis

Due to the nature of research questions hypothesized for this present study, one quantitative and two qualitative research questions, and because of the mixed-method research design model, QUAN-Qual Model, all data were also analyzed with two types of data analysis methods, quantitative and qualitative.

The first phrase was comprised of quantitative data analysis, and the second phrase was comprised of qualitative data analysis. However, in this study, the data analysis was more heavily weighted toward the quantitative side, and the qualitative analysis helped explain or elaborate the quantitative results by serving triangulation for the quantitative data results. 4.2.1. Quantitative Data Analysis

As the first step of the quantitative data analysis method in this study, a variety of descriptive

statistics were involved. First, to do this, a data file was made to analyze all raw data collected from subjects and was analyzed because all subjects received the same amount of treatments during the eight weeks.

As one descriptive statistics, the overall mean scores (Mean) were calculated, which basically showed the average performance in a test group on a measure of segmentals and suprasegmentals about the target pronunciation and also provided this study with the baseline of how much improvement occurred in each group as a result of the treatment, and how much a significant difference was in overall learning outcomes and between tests. The other important descriptive statistics used for quantitative data analysis were the standard deviation (SD), which showed how spread out a set of scores was around mean, and displayed whether the scores were relatively homogeneous or heterogeneous around the mean. Additional descriptive statistic calculated in this

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study were corrected percentage (%) of the target pronunciation of the segmentals and suprasegmentals in all two tests in all subjects in which how much improvement occurred in the overall learning results were indicated.

As another step for quantitative data analysis, the t-tests for independent and dependent samples as an inferential statistic were carried out because one of the purposes of this present study was to investigate whether there would be a significant difference (*p<.05) between tests as a result of the robot iRobiQ,’s segmental and suprasegmental pronunciation training. In this study, the t-tests for the dependent and independent samples were calculated by using the SPSS 17.5 version for all tests.

4.2.2. Qualitative Data Analysis

As a second phrase of the data analysis in this study, a qualitative data analysis was conducted. The main purpose of the qualitative data analysis in this study was to elaborate, to triangulate the quantitative results, and to discover the reasons for the results from the subjects' and researchers' points of view. To achieve this aim, all qualitative data obtained from the open-ended survey questions of survey questionnaires were analyzed, classified, and described.

More specifically, the researcher read all collected information to understand the overall data and reduced the data by creating displays of all collected information such as tables as means of visualizing the information and representing it by themes. Then, the researcher classified the reduced information into general themes related to the research questions. Finally, the researcher interpreted and presented the data by describing students' perceptions that compared the subjects' perceptions of robot iRobiQ and its pronunciation training. This description contained text, not numbers. The data analysis method in this study showed inductive analysis that began with the raw data consisting of multiple sources of information and then broadened to several specific themes [23].

5. Results 5.1. Quantitative Findings

To examine a significant difference in overall learning outcomes of the subjects (N = 18) between

pretest and posttest, the t-tests of dependent samples were conducted on the mean scores measured in two tests. The results of the descriptive and inferential statistic calculated from the mean scores in the group are reported in Table 3.

The major findings show that students (N = 18) significantly increased their overall learning outcomes between pretest and posttest. A comparison of students' pretest and posttest displayed a statistically significant improvement (t = 6.554, *p = .0002) in their segmental features as a result of robot, iRobiQ, segmental feature pronunciation training.

Table 3. Descriptive and Inferential Statistics on Overall Scores of Segmental Features

Comparison Tests

Raw Scores (N = 1764)

Experimental Group (N = 18)

Difference

Segmentals Corrected (%)

Mean (N = 98)

SD t-statistic p-value

Pretest vs. Posttest Pretest 733(41.6%) 40.7 19.41

6.554 .0002* Posttest 1084(61.5%) 60.2 19.29

Note: *Statistically significant = p<.05. Raw Scores (N = 1764) = (N = 18) X 98 Words.

To investigate the 4th grade students' (N = 7) overall learning outcomes of robot iRobiQ pronunciation training, the raw scores and percentages of correct answers on segmental feature test (N = 686) in the subjects were measured. Subsequently, paired t-tests of dependent samples were conducted to test a significant difference between pretest and posttest. Table 4 reports the summary measures and significant tests on segmentals in the 4th and 5th grade students.

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With respect to overall learning outcomes of the 4th grade students' segmental features, the major

findings show that there was a significant difference between pretest and posttest (t = 5.640, *p = .0006) as a result of the treatments of robot pronunciation training.

Also, to examine a significant difference in overall learning outcomes between pretest and posttest of the 5th grade students (N = 11), the t-tests of dependent samples were carried out on the combined mean scores of segmentals measured in two tests.

With the 5th grade students, the comparison between pretest and posttest indicated a statistically significant difference (t = 4.347, *p = .0007) as a result of robot iRobiQ segmental pronunciation training. The results of the descriptive and inferential statistics calculated from the mean scores of the segmental test in the experimental group are reported in Table 4.

Table 4. Comparison of Descriptive and Inferential Statistics on Overall Scores of Segmental

Features by the 4th and 5th Grade

Comparison Tests

Raw Scores (N = 686, N = 1078)

Experimental Group Difference

Segmentals Corrected (%)

Mean (N = 98)


Pretest vs. Posttest

4th Grade (N = 7)

Pretest 328(30.4%) 46,7 19.29 5.640 .0006*

Posttest 465(43.1%) 66.4 16.07

5th Grade (N = 11)

pretest 405 (37.6%) 36.82 19.33 4.347 .0007*

posttest 619 (57.4%) 56.27 20.82

Note: Statistically significant *p<.05. Raw Scores (N = 686) = (N = 7) X 98 Words, Raw Scores (N = 1078) = (N = 11) X 98 Words

Table 5 summarizes the descriptive and inferential statistics conducted to test whether there would

be a significant difference on the gain scores of the suprasegmentals, intonation, in the posttest subtracted by the pretest (posttest-pretest).

The major findings show that students significantly had higher learning outcomes. More specifically, the results on the overall learning outcomes between two tests of the students display that there was a significant difference (t = 7.949, *p = .0001) between pretest and posttest as a result of robot pronunciation training.

Table 5. Descriptive and Inferential Statistics on Overall Scores of Suprasegmental Features

Comparison Tests

Raw Scores (N = 540)

Experimental Group (N = 18)

Difference

Suprasegmentals Corrected (%)

Mean (N = 30)



Pretest 142 (26.3%) 7.89 3.97 7.949 .0001*

Posttest 278 (51.5%) 15.44 5.07

Note: Statistically significant *p<.05. Raw Scores (N = 540) = (N= 18) X 30 Suprasegmentals. Table 6 displays the descriptive and inferential statistics carried out to investigate whether there

would be a significant difference between pretest and posttest after all treatments of suprasegmentals from robot iRobiQ for the 4th grade students.

The 4th grade students had higher test scores in the posttest as a result of their suprasegment pronunciation training with robot iRobiQ. The major findings indicate that there was a significant difference in the improvement between pretest and posttest (t = 3.627, *p = .005) as a result of the treatments of robot iRobiQ pronunciation training in the 4th grade students.

In addition, Table 6 shows the results of the statistics measured to test whether there would be a significant difference between overall learning outcomes after all treatments in the 5th grade students. As shown in Table 6, the main findings display that there was a significant difference (t = 7.760, *p = .0007) in overall learning outcomes of robot iRobiQ pronunciation training in the 5th grade students.

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Table 6. Comparison of Descriptive and Inferential Statistics on Overall Scores of Suprasegmental

Features in the 4th and 5th Grade

Comparison Tests

Raw Scores (N = 210, N = 330)

Experimental Group(N = 7)

Difference

Suprasegmentals Corrected (%)

Mean (N = 30)



4th Grade (N = 7)

Pretest 69 (32.9%) 9.86 5.11 3.627 .005*

Posttest 119 (56.7%) 17.00 5.77

5th Grade (N = 11)

Pretest 73 (22.1%) 6.64 2.58 7.760 .0007*

Postest 159 (48.2%) 14.45 4.57

Note: *Statistically significant p<.05. Raw Scores (N = 210) = (N = 7) X 30

Table 7 summarizes the descriptive and inferential statistics conducted to test whether there would be a statistically significant difference between segmental feature pretest and posttest scores and suprasegmental feature pretest and posttest scores. A comparison of students' two pretests (t = 4.231, *p = .0002) and posttests (t = 2.029, *p = .0292) showed statistically significant improvement in their overall learning outcomes toward robot segmental and suprasegmental pronunciation training.

Also, Table 7 summarizes the descriptive and inferential statistics measured to investigate whether there would be a significant difference in two posttests in all subjects (N = 18) after all treatments from robot iRobiQ. The main findings show that there was a significant difference between two posttests. Also, the results report that students had higher overall learning outcomes in the segmental posttest (61.5%) than those in the suprasegmental posttest (51.5%).

Table 7. Comparison of Descriptive and Inferential Statistic between Pretests of Segmentals and

Suprasegmentals

Comparison Tests Segmentals Suprasegmentals Difference

Mean(%) SD Mean(%) SD t-statistic p-value Pretest vs. Pretest Pretests 41.5% 19.81 26.2% 13.22 4.231 .0002* Posttest vs. Posttest Posttests 61.5% 19.68 51.5% 16.88 2.029 .0292*

Note: Statistically significant *p<.05

Table 8 summarizes the results of the statistics conducted to compare the overall learning outcomes between the pretest and the posttest of segmentals and suprasegmentals after all treatments from robot iRobiQ. The main findings show that there was no significant difference (t = 1.323, p = .203) between the overall improvement of the segmental feature test scores and the improvement of the suprasegmental feature test scores, and that students had better results in word-segment posttest (Mean Improvement = 19.9%) than those in sentence-segment posttest (Mean Improvement = 25.2%) as compared to mean score improvement.

Table 8. Comparison of Overall Learning Improvement of Segmentals and Suprasegmentals

Comparison Segmentals Suprasegmentals Difference

Mean Improvement

MD SD Mean

ImprovementMD SD t-statistic p-value

Posttest - Pretest = Improvement

19.9% 20.4% 12.9% 25.2% 26.7% 13.4% 1.323 .203

Note: Not statistically significant p>.05

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5.2. Student Questionnaire Likert Scales

5.2.1. Students' Perceptions of Robot iRobiQ

The major findings show that the robot iRobiQ was preferred by most of the students who participated in this current study. More importantly, the results were rated as favorable by showing the overall average Likert scale 4 out of 5, in which Likert scale 1 means "strongly disagree", and Likert scale 5 means "strongly agree", on the aspect and perceptions of activities and treatments involved with robot iRobiQ. Moreover, as shown in Table 9, the students (N = 18) tended to consider iRobiQ to be helpful and useful for their English learning. More specifically, more than 70% of the students who answered in their perceptions about robot iRobiQ, indicated positive perspectives and attitudes towards the aspects of the activities and treatments of the -robot iRobiQ, while a few of the students (16.7%) participated in this current study showed negative aspects and disagreement about the activities and treatments with robot iRobiQ. These results demonstrate that using robot iRobiQ in the field of education, esp., second language learning, could be more useful, helpful, and effective than others in elementary school students.

Table 9. Students' Perceptions of Humanoid-Robot, iRobiQ Students' Perception of Humanoid-robot, iRobiQ (N = 18)

Frequency Times (N = 18) and Percentages (%) Average Likert Scale Question Items 1 2 3 4 5

Q1. I learned a lot from robot iRobiQ. (2)

11.1%(2)

11.1%(2)

11.1% (8)

44.4%(4)

22.2% 3.6

Q2. I enjoyed many activities with the robot iRobiQ. (1)

5.6% (1)

5.6% (1)

5.6% (9)

50.0%(6)

30.0% 4.0

Q3. The robot, iRobiQ was helpful for learning English pronunciation.

(1) 5.6%

(2) 11.1%

(2) 11.1%

(9) 50.0%

(4) 22.2%

3.7

Q4. I consider the robot iRobiQ to be a useful tutor for learning English pronunciation.

(1) 5.6%

(2) 11.1%

(2) 11.1%

(8) 44.4%

(5) 22.2%

3.8

Q5. The learning environment with robot, iRobiQ, was more supportive than a regular English class.

(1) 5.6%

(1) 5.6%

(1) 5.6%

(9) 50.0%

(6) 30.0%

4.0

Q6. The robot, iRobiQ, strengthened my English learning experience.

(0) 0.0%

(2) 11.1%

(2) 11.1%

(8) 44.4%

(6) 30.0%

4.0

Q7. There was less competitive in robot iRobiQ, settings than in those I studied in a regular class.

(0) 0.0%

(0) 0.0%

(1) 5.6%

(10) 55.6

(7) 38.9

4.3

Q8. The experience in robot, iRobiQ, made me more motivated in English learning.

(1) 5.6%

(1) 5.6%

(2) 11.1%

(8) 44.4%

(6) 30.0%

3.9

Q9. It was fun for me to learn English with robot iRobiQ.(0)

0.0% (0)

0.0% (1)

5.6% (11)

61.1%(6)

30.0% 4.3

Average Scores 4.3% 6.8% 8.7% 49.4% 28.4% 4.0

Note: 1 = strongly disagree 2 = disagree 3 = neutral 4 = agree 5 = strongly agree

5.2.2. Students' Perceptions of Robot iRobiQ’s Pronunciation Training

The overall findings show that most of the students (69.3%) who participated in this current study consider robot iRobiQ’s pronunciation training effective and helpful. As shown in Table 10, with respect to the questions asking the students' perceptions about robot iRobiQ’s pronunciation training, the overall average Likert scale was measured as 3.8 out of 5, in which Likert scale 1 means "strongly disagree," and Likert scale score 5 means "strongly agree”. On the other hand, some of the students answered to the questions showed negative (16.7%) or neutral (8.4%) perceptions and attitudes towards robot iRobiQ pronunciation training.

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Table 10. Students' Perception of robot iRobiQ’s Pronunciation Training

Students' Perception of Humanoid-robot, iRobiQ, Pronunciation Training (N = 18) Frequency Times(N = 18) and Percentages(%) Average

Likert Scale Question Items 1 2 3 4 5

Q1. I learned a lot of English pronunciation from robot pronunciation training.

(2) (11.1%)

(2) 11.1%

(2) 11.1%

(5) 22.2%

(8) 44.4%

4.0

Q2. I liked English word and sentence pronunciation training with robot iRobiQ.

(0) 0.0%

(2) 11.1%

(2) 11.1%

(9) 50.0%

(5) 22.2%

3.9

Q3. I learned more accurate English word pronunciation from robot iRobiQ’s pronunciation training than I would have learned in a regular English class.

(2) 11.1%

(3) 16.7%

(1) 5.6%

(8) 44.4%

(4) 22.2%

3.5

Q4. I learned more accurate English sentence pronunciation from robot iRobiQ’s pronunciation training than I would have learned in a regular English class.

(1) 5.6%

(1) 5.6%

(2) 11.1%

(9) 50.0%

(5) 22.2%

3.9

Q5. I learned more accurate English word pronunciation than English sentence pronunciation from robot iRobiQ’s pronunciation training.

(2) 11.1%

(2) 11.1%

(1) 5.6%

(8) 44.4%

(5) 22.2%

3.7

Q6. The robot iRobiQ pronunciation training contributed greatly to my English pronunciation improvement.

(1) 5.6%

(2) 11.1%

(2) 11.1%

(9) 50.0%

(4) 22.2%

3.7

Q7. I continuously want to learn more English pronunciation with robot iRobiQ.

(1) 5.6%

(1) 5.6%

(1) 5.6%

(9) 50.0%

(6) 30.0%

4.0

Q8. I would like to learn more pronunciation in the future with robot iRobiQ.

(1) 5.6%

(1) 5.6%

(1) 5.6%

(9) 50.0%

(6) 30.0%

4.0

Average Scores 7.0% 9.7% 8.4% 45.1% 24.2% 3.8

Note: 1 = strongly disagree 2 = disagree 3 = neutral 4 = agree 5 = strongly agree

5.3. Qualitative Findings

5.3.1 Open-Ended Survey Questions

This section describes key themes found in students' perceptions about robot iRobiQ as an instructional assistant and its pronunciation training which were elicited from the open-ended survey questions administered to the students at the end of all treatments in order to investigate whether students perceive the robot pronunciation training as effective in the pronunciation learning and what students' Perceptions are about robot iRobiQ.

5.3.1.1. Students' Perceptions about robot iRobiQ

Most of the students believed that they felt comfortable and had fun. Also, because iRobiQ has various kinds of ample contents and programs related to educational activities, students said that they would like to study with iRobiQ through short-term and long-term period of time. Moreover, with respect to the competitive learning environment of traditional teacher-led approaches, most of the students showed negative feedback and reflection because they felt safer, less competitive, and had more fun with iRobiQ. Furthermore, the findings displayed that most of the students could do more activities and tasks with iRobiQ. For instance, one of the subjects reflected as follows:

I think that all my friends enjoyed iRobiQ. Individually, I preferred iRobiQ to my English teacher because iRobiQ was so fun and exciting to me. So, if I have iRobiQ at home, I would like to do more things with iRobiQ.

One of the many reasons why students expressed stronger interaction between them and robot iRobiQ was that more interaction occurred between students and iRobiQ because iRobiQ offered more opportunities for students to be exposed during the experimental period of time. Also, many students believed that activities with robot iRobiQ made them more productive because activities

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provided more chances for input and output English not only in class but also outside the classrooms as extended class activities. For this reason, most of the students confided that more learning occurred through the robot iRobiQ. For example, one of the students reflected as follows:

First of all, I really liked iRobiQ inside and outside classrooms. Also, because I could meet iRobiQ on every school day, I had more opportunities to practice English with iRobiQ. Especially, unlike my English teacher whom I could only meet in English classes, iRobiQ could see iRobiQ anytime, and gave me a lot of exciting and interesting activities that are useful for English learning. Hence, I believe that I learned much more from iRobiQ than I did from my English teachers.

On the other hand, the findings showed that a few of the students had neutrality or negative perceptions of robot iRobiQ. These students reported that they had a little boring time with iRobiQ because its voice and sound are not clearer than a teacher's, and because they had to touch iRobiQ's screen or the monitor to practice English with iRobiQ. Also, they did not have more distinguished sound and voice from a CD or a listening tape. For example, one of the students explained as follows:

In my case, my position is neutral because I could not have much more interest in iRobiQ, so I could have more or less negative or similar effectiveness on word and sentence pronunciation in iRobiQ and teachers. Individually, I would like to say that studying English with iRobiQ could be a lot fun, but in my case, I would like to study English with my English teacher.

5.3.1.2. Students' Perceptions about robot iRobiQ’s Pronunciation Training

The findings showed that most of the students had more learning opportunities to practice pronunciation with robot iRobiQ than with the regular class. Also, many students reported that they themselves could find out their pronunciation mistakes and errors as compared to iRobiQ's pronunciation, so they could fix their pronunciation by themselves. Therefore, almost all students believed that iRobiQ helped them improve their pronunciation together with fun and a safe environment. Furthermore, the findings demonstrate that with iRobiQ's pronunciation training, students can become more independent and active learners, and they could increase their accurate pronunciation for the target pronunciation.

6. Conclusion and Discussion

The present study was undertaken to investigate whether there were statistically significant differences between before- and after robot iRobiQ’s pronunciation training about segmental and suprasegmental features to elementary school students (N = 18). To this end, segmentals and suprasegmentals which are the most problematic to Korean speakers were chosen and tested: 8 consonants and 6 vowels as segmentals, and 6 types of intonation patterns as suprasegmentals [11].

The first research question asked whether there would be a significant difference between before- and after robot iRobiQ’s segemental and suprasegmental feature training to Korean EFL elementary school students. The results in this current study indicate that there were statistically significant differences before- and after robot iRobiQ,s pronunciation training in both segmentals (t = 6.554, *p<.05) and suprasegmentals (t = 7.949, *p<.05). More importantly, one of many major findings shows that while the overall learning outcomes of both segmentals and suprasegmentals pronunciation training significantly increased as a result of the treatments, there was no significant difference (t = 1.323, p>.05) between the improvement of segmental posttest scores and suprasegmental posttest scores. However, as shown in Table 13, students had a higher overall learning outcome improvement (25.2%) in suprasegmentals than that (19.9%) in segmentals of the target pronunciation.

Also, both pretests and posttests about segmentals and suprasegmental pronunciation showed statistically significant differences, but the pretests showed more significant differences (t = 4.230, *p = .0008) than the posttests (t = 2.029, *p = .0292). However, due to a higher correct mean percentage in pretest of segmentals (41.6%) than that in the pretest of suprasegmentals (26.3%), students had higher metalinguistic and metacognitive knowledge in segmentals than that in suprasegmentals. According to many previous studies [24], [25], [26], [27], [28], [4] conducted to investigate the effectiveness of integrating pronunciation into ESL/EFL classrooms, it is desirable to address pronunciation teaching in the context of speaking, and a speaking-oriented approach serves the

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communication needs and beliefs of students more effectively than approaches focusing on either fluency or articulatory goal alone [4]. Considering the results of the pretests of this current study in segmentals and suprasegmentals, this study assumes that students and teachers have more preferred approaches based on segmental pronunciation training, and that teachers have had little clear direction and approaches about how to integrate suprasegmentals into meaningful communication. This result also suggests that for the most part, the activities should focus more on suprasegmental features "because segmental features are not important in communication, and because suprasegmentals are more clearly connected to functions of spoken English" [4].

In addition, in posttests, students displayed higher overall learning score percentages in segmentals (61.5%) than those in suprasegmentals (51.9%). These results indicate that students' segmental metacognitive and metalinguistic knowledge might more influence the overall learning outcomes. However, as shown in Table 13, students' overall learning improvement about segmental and suprasegmental target pronunciation reported that the robot iRobiQ would be more effective in suprasegmental pronunciation training (25.2%) than in segmental pronunciation(19.9%) in the results of all tests.

Table 11. Comparison of Overall Learning Improvement between Pretest and Posttest in Segmentals and Suprasegmentals

Comparison Segmentals Suprasegmentals Difference

Mean (Corrected %)

SD Mean

(Corrected %)SD t-statistic p-value

Pretest 41.6% 19.80 26.3% 16.66 4.230 .0008* Posttest 61.5% 19.68 51.9% 16.89 2.029 .0292*

Posttest - Pretest = Improvement

19.9% 25.2% 1.323 .2032

Note. *Statistically significant p<.05

The second and third research question asked the perceptions about robot iRobiQ and its pronunciation training. While utilizing robot iRobiQ as a learning assistant, students were able not only to improve both their pronunciation proficiency level, but also to enhance their self-confidence and independence. In addition, most of the students felt more comfortable and less competitive. Moreover, they had more chances to contact iRobiQ in terms of showing more positive perceptions about iRobiQ (Likert Average Scale 4.0 out of 5) and its pronunciation training (Likert Average Scale 3.8 out of 5) and interest in iRobiQ and its contents and programs. These results of Likert scales show that students liked more iRobiQ than its pronunciation training. Simultaneously, these results positively suggest a new paradigm and implications towards using robots in the field of second or foreign language pedagogy and encourage students to increase their overall learning motivation and positive attitudes towards second or foreign language learning. Consequently, as shown in all qualitative results obtained from this current study, all this led students to higher proficiency in their pronunciation training with robot, iRobiQ.

The overall results illustrate the prospectus nature of employing robots as pedagogical assistants and tutors for second language acquisition. Despite very limited robot, iRobiQ, contents and short period of time of 8 weeks, this study concludes that this robot-assisted pronunciation training approach can help students enhance their pronunciation competency and proficiency level and employ these segmental and suprasegmental features in their real and authentic language use. At the same time, using the robots as pedagogical tutors appears to change traditional pronunciation pedagogical paradigm into a new robot-based learning paradigm. As have been seen from the results of many of the very recent studies [8], [6], [18], [29], [3] carried out to investigate the educational effectiveness of various kinds of robots in the various fields of education such as English language, science, and music, the learning process in child education programs has become a robot-based r-learning approach. Furthermore, based on the results and findings of this current study, this study not only reports that robots can serve as classmates or partners to students, and that they can provide more meaningful and relevant environments or settings for elementary school students. While for elementary school students to learn to employ a robot as a learning assistant or tool still have a small

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step at a time, the development of more ample robot contents can more stimulate children's second language acquisition and increase students' actual learning.

On the practical level, if robots are to be implemented as pedagogical assistants in the field of second language acquisition for L2 pronunciation competency and proficiency or infused in all L2 courses in elementary schools, challenges still remain. First, to make robots more useful and successful instructional assistants or tutors inside and outside classrooms, all L2 teachers need to be provided with continuous and up-to-date pedagogical r-learning technology training. With the very fast advancement of robotics and its technology, it will not be easy for producing those who are educated pedagogically well in the field of robotics. Thus, teachers' r-learning training programs in that field should be developed constantly. Moreover, in order to prepare teachers who can suitably serve in robot teaching environments, more detailed and specific curriculums have to be developed. Second, for the students who can have more or less negative perceptions of robots as shown by some of the students' perceptions in this current study, more motivated and interesting software programs and contents need to be designed and developed. From the results of this current study, a few of the students showed a little negative perceptions and attitudes towards robot education, while their overall learning outcome is significantly better than before. Thus, if robots as instructional tools are set in public K-1 through K-6 classrooms, alternatives which can minimize these negative perceptions and attitudes need to be prepared and studied. 7. Acknowledgement

This work was supported by the R&D program of KEIT (Korea Evaluation Institute of Industrial

Technology)/MKE. Also, this work was published in proceeding of 2010 KAPEE International Conference, Seoul, Korea, pp.128-147, Jan., 2010.

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