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Are Tutor Robots for Everyone? The Influence of Attitudes, Anxiety, and Personality on Robot-Led Language Learning

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Abstract

Do some individuals benefit more from social robots than others? Using a second language (L2) vocabulary lesson as an example, this study examined how individual differences in attitudes toward robots, anxiety in learning L2, and personality traits may be related to the learning outcomes. One hundred and two native Turkish-speaking adults were taught eight English words in a one-on-one lesson either with the NAO robot (N = 51) or with a human tutor (N = 51). The results in both production and receptive language tests indicated that, following the same protocol, the two tutors are fairly comparable in teaching L2 vocabulary. Negative attitudes toward robots and anxiety in L2 learning impeded participants from learning vocabulary in the robot tutor condition whereas the personality trait of extroversion negatively predicted vocabulary learning in the human tutor condition. This study is among the first to demonstrate how individual differences can affect learning outcomes in robot-led sessions and how general attitudes toward a type of device may affect the ways humans learn using the device.

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Notes

  1. We offered two forms of compensation to increase the diversity of our subject pool. A GLMM testing the form of compensation (money vs. course credit) as the sole fixed factor and word as a random intercept suggests that the form of compensation did not affect the scores of any of the outcome measures (p = 0.828 for the immediate production test; p = 0.900 for the immediate receptive test; p = 0.927 for the delayed production test; and p = 0.275 for the delayed receptive test).

  2. We did not use the default text-to-speech (TTS) library in NAO because native Turkish speakers in the research team (co-authors and research assistants) found the Turkish speech to be unnatural and difficult to comprehend. The Amazon Polly “Filiz” was the most natural Turkish option we found, and “Salli” was chosen for English speech as it sounded most similar to “Filiz” among available options. We also modified the input text when the generated speech was unnatural or difficult to comprehend.

  3. We used GLMMs in these analyses because they can be more powerful than parametric tests such as an ANOVA that assumes a normal distribution, as they allow us to analyze the responses of participants without averaging across trials [48]. As the outcome (the scores of the four post-lesson tests) was a binary variable (correct vs. incorrect), logit (log-odds) was used as the link function. The GLMMs constructed here also tested by-item random intercept to ensure that no effect is driven by specific test items. GLMMs were generated in R [49] using the lme4.glmer function [50]. In all models, we included the random effect of item (e.g., L2 words) as some L2 vocabulary words may be inherently more difficult to learn than others. All models were fit by maximum likelihood using adaptive Gauss-Hermite quadrature (nAGQ = 1).

References

  1. Ortega L (2009) Understanding second language acquisition. Routledge, Taylor & Francis Group, London

    Google Scholar 

  2. Bartneck C, Forlizzi J (2004) A design-centred framework for social human-robot interaction. In: RO-MAN 2004: 13th IEEE international workshop on robot and human interactive communication. Institute of Electrical and Electronics Engineers, Kurashiki, Japan, pp 591–594

  3. Dore RA, Zosh JM, Hirsh-Pasek K, Golinkoff RM (2017) Chapter 4—Plugging into word learning: the role of electronic toys and digital media in language development. In: Blumberg FC, Brooks PJ (eds) Cognitive development in digital contexts. Academic Press, San Diego, pp 75–91

    Chapter  Google Scholar 

  4. Han J-H, Jo M-H, Jones V, Jo J-H (2008) Comparative study on the educational use of home robots for children. J Inf Process Syst 4:159–168. https://doi.org/10.3745/JIPS.2008.4.4.159

    Article  Google Scholar 

  5. Kennedy J, Baxter P, Belpaeme T (2015) Comparing robot embodiments in a guided discovery learning interaction with children. Int J Soc Robot 7:293–308

    Article  Google Scholar 

  6. Kanero J, Geçkin V, Oranç C et al (2018) Social robots for early language learning: current evidence and future directions. Child Dev Perspect 12:146–151. https://doi.org/10.1111/cdep.12277

    Article  Google Scholar 

  7. Belpaeme T, Kennedy J, Ramachandran A et al (2018) Social robots for education: a review. Sci Robot 3:eaat5954. https://doi.org/10.1126/scirobotics.aat5954

    Article  Google Scholar 

  8. Bloom BS (1984) The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ Res 13:4–16. https://doi.org/10.3102/0013189X013006004

    Article  Google Scholar 

  9. Leyzberg D, Spaulding S, Scassellati B (2014) Personalizing robot tutors to individuals’ learning differences. In: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction. ACM, pp 423–430

  10. Schodde T, Bergmann K, Kopp S (2017) Adaptive robot language tutoring based on Bayesian knowledge tracing and predictive decision-making. In: Proceedings of the 2017 ACM/IEEE international conference on human-robot interaction. Association for Computing Machinery, New York, NY, pp 128–136

  11. Chamorro-Premuzic T, von Stumm S, Furnham A (2015) The Wiley-Blackwell handbook of individual differences. Wiley-Blackwell, Malden, MA

    Google Scholar 

  12. Kidd E, Donnelly S, Christiansen MH (2018) Individual differences in language acquisition and processing. Trends Cogn Sci 22:154–169. https://doi.org/10.1016/j.tics.2017.11.006

    Article  Google Scholar 

  13. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340. https://doi.org/10.2307/249008

    Article  Google Scholar 

  14. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478. https://doi.org/10.2307/30036540

    Article  Google Scholar 

  15. Barnett T, Pearson AW, Pearson R, Kellermanns FW (2015) Five-factor model personality traits as predictors of perceived and actual usage of technology. Eur J Inf Syst 24:374–390. https://doi.org/10.1057/ejis.2014.10

    Article  Google Scholar 

  16. Ivaldi S, Lefort S, Peters J et al (2017) Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human–robot assembly task: experiments with the iCub humanoid. Int J Soc Robot 9:63–86. https://doi.org/10.1007/s12369-016-0357-8

    Article  Google Scholar 

  17. Tapus A, Ţăpuş C, Matarić MJ (2008) User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intel Serv Robot 1:169. https://doi.org/10.1007/s11370-008-0017-4

    Article  Google Scholar 

  18. Gockley R, Matarić MJ (2006) Encouraging physical therapy compliance with a hands-off mobile robot. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on human–robot interaction. Association for Computing Machinery, Salt Lake City, UT, pp 150–155

  19. Salem M, Lakatos G, Amirabdollahian F, Dautenhahn K (2015) Would you trust a (faulty) robot? Effects of error, task type and personality on human–robot cooperation and trust. In: 2015 10th ACM/IEEE international conference on human–robot interaction (HRI), pp 1–8

  20. Takayama L, Pantofaru C (2009) Influences on proxemic behaviors in human-robot interaction. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, pp 5495–5502

  21. Robert L (2018) Personality in the human robot interaction literature: a review and brief critique. Social Science Research Network, Rochester, NY

    Google Scholar 

  22. der Pütten AR, Weiss A (2015) The uncanny valley phenomenon: does it affect all of us? Interact Stud 16:206–214. https://doi.org/10.1075/is.16.2.07ros

    Article  Google Scholar 

  23. Kanda T, Hirano T, Eaton D, Ishiguro H (2004) Interactive robots as social partners and peer tutors for children: a field trial. Hum-Comput Interact 19:61–84

    Article  Google Scholar 

  24. Robins B, Dautenhahn K, Boekhorst RT, Billard A (2005) Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? Univ Access Inf Soc 4:105–120. https://doi.org/10.1007/s10209-005-0116-3

    Article  Google Scholar 

  25. Nomura T, Kanda T, Suzuki T, Kato K (2008) Prediction of human behavior in human–robot interaction using psychological scales for anxiety and negative attitudes toward robots. IEEE Trans Robot 24:442–451. https://doi.org/10.1109/TRO.2007.914004

    Article  Google Scholar 

  26. Debreli E, Oyman N (2016) Students’ preferences on the use of mother tongue in English as a foreign language classrooms: is it the time to re-examine English-only policies? Engl Lang Teach 9:148–162

    Article  Google Scholar 

  27. Liu J (2008) L1 use in L2 vocabulary learning: facilitator or barrier. Int Educ Stud 1:65–69

    Article  Google Scholar 

  28. Horwitz EK, Horwitz MB, Cope J (1986) Foreign language classroom anxiety. Mod Lang J 70:125. https://doi.org/10.2307/327317

    Article  Google Scholar 

  29. Horwitz E (2001) Language anxiety and achievement. Annu Rev Appl Linguist 21:112–126. https://doi.org/10.1017/S0267190501000071

    Article  Google Scholar 

  30. Alemi M, Meghdari A, Basiri NM, Taheri A (2015) The effect of applying humanoid robots as teacher assistants to help Iranian autistic pupils learn English as a foreign language. In: Tapus A, André E, Martin JC, Ferland F, Ammi M (eds) Social robotics. ICSR 2015. Lecture Notes in Computer Science, vol 9388. Springer, Cham

    Google Scholar 

  31. Ożańska-Ponikwia K (2017) Extraverts and introverts in the FL classroom setting. In: Second language learning and teaching, pp 93–105

  32. Dornyei Z, Ryan S (2015) The psychology of the language learner revisited. Routledge, New York, NY

    Book  Google Scholar 

  33. Dewaele J-M (2012) Personality in second language acquisition. In: The encyclopedia of applied linguistics. American Cancer Society

  34. Naiman N (1978) The good language learner: a report. Ontario Institute for Studies in Education, Toronto

    Google Scholar 

  35. Ehrman M (2008) Personality and good language learners. In: Griffiths C (ed) Lessons from good language learners. Cambridge University Press, Cambridge, pp 61–72

    Chapter  Google Scholar 

  36. Carrell PL, Prince MS, Astika GG (1996) Personality types and language learning in an EFL context. Lang Learn 46:75–99. https://doi.org/10.1111/j.1467-1770.1996.tb00641.x

    Article  Google Scholar 

  37. Ellis R (2004) Individual differences in second language learning. In: The handbook of applied linguistics. Blackwell, Malden, MA, pp 525–551

  38. Dewaele J-M, Furnham A (1999) Extraversion: the unloved variable in applied linguistic research. Lang Learn 49:509–544. https://doi.org/10.1111/0023-8333.00098

    Article  Google Scholar 

  39. MacIntyre PD, Clement R, Noels KA (2007) Affective variables, attitude and personality in context. In: Ayoun D (ed) French applied linguistics. John Benjamins Publishing Company, Amsterdam, pp 270–298

    Chapter  Google Scholar 

  40. Wakamoto N (2009) Extroversion/introversion in foreign language learning: interactions with learner strategy use. Peter Lang, Bern

    Google Scholar 

  41. Kotov R, Gamez W, Schmidt F, Watson D (2010) Linking “big” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychol Bull 136:768–821. https://doi.org/10.1037/a0020327

    Article  Google Scholar 

  42. Paulus DJ, Vanwoerden S, Norton PJ, Sharp C (2016) From neuroticism to anxiety: examining unique contributions of three transdiagnostic vulnerability factors. Personal Individ Differ 94:38–43. https://doi.org/10.1016/j.paid.2016.01.012

    Article  Google Scholar 

  43. Nomura T, Kanda T, Suzuki T (2006) Experimental investigation into influence of negative attitudes toward robots on human–robot interaction. AI Soc 20:138–150. https://doi.org/10.1007/s00146-005-0012-7

    Article  Google Scholar 

  44. Aydın S, Harputlu L, Güzel S et al (2016) A Turkish version of foreign language anxiety scale: reliability and validity. Procedia Soc Behav Sci 232:250–256. https://doi.org/10.1016/j.sbspro.2016.10.011

    Article  Google Scholar 

  45. Demir B, Kumkale GT (2013) Individual differences in willingness to become an organ donor: a decision tree approach to reasoned action. Personality Individ Differ 55:63–69. https://doi.org/10.1016/j.paid.2013.02.002

    Article  Google Scholar 

  46. Dunn LM, Dunn DM (2007) Peabody picture vocabulary test—fourth edition (PPVT). NCS Pearson, Minneapolis, MN

    Google Scholar 

  47. Dahlbäck N, Jönsson A, Ahrenberg L (1993) Wizard of Oz studies—why and how. Knowl-Based Syst 6:258–266. https://doi.org/10.1016/0950-7051(93)90017-N

    Article  Google Scholar 

  48. Jaeger TF (2008) Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models. J Mem Lang 59:434–446. https://doi.org/10.1016/j.jml.2007.11.007

    Article  Google Scholar 

  49. R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  50. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01

    Article  Google Scholar 

  51. Shin D, Chon YV, Kim H (2011) Receptive and productive vocabulary sizes of high school learners: what next for the basic word list? Engl Teach 66:127–152

    Google Scholar 

  52. Vogt P, van den Berghe R, de Haas M et al (2019) Second language tutoring using social robots: A large-scale study. In: 2019 14th ACM/IEEE international conference on human–robot interaction (HRI), pp 497–505

  53. Hinz NA, Ciardo F, Wykowska A (2019) Individual differences in attitude toward robots predict behavior in human–robot interaction. In: Salichs M et al (eds) Social robotics. ICSR 2019. Lecture Notes in Computer Science, vol 11876. Springer, Cham. https://doi.org/10.1007/978-3-030-35888-4_7

    Chapter  Google Scholar 

  54. Daly J (1991) Understanding communication apprehension: an introduction for language educators. In: Horwitz EK, Young DJ (eds) Language anxiety: from theory and research to classroom implications. Prentice Hall, Englewood Cliffs, NJ, pp 3–14

    Google Scholar 

  55. Kennedy J, Baxter P, Belpaeme T (2015) The robot who tried too hard: social behaviour of a robot tutor can negatively affect child learning. In: Proceedings of the tenth annual ACM/IEEE international conference on human–robot interaction. Association for Computing Machinery, Portland, OR, pp 67–74

  56. Clenton J, Booth P (2021) Vocabulary and the four skills: pedagogy, practice, and implications for teaching vocabulary. Routledge, Abingdon

    Google Scholar 

  57. Goetz J, Kiesler S, Powers A (2003) Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: The 12th IEEE international workshop on robot and human interactive communication, 2003. Proceedings. ROMAN 2003, pp 55–60

  58. Kanda T, Miyashita T, Osada T et al (2008) Analysis of humanoid appearances in human–robot interaction. IEEE Trans Rob 24:725–735. https://doi.org/10.1109/TRO.2008.921566

    Article  Google Scholar 

  59. Schermerhorn P, Scheutz M, Crowell CR (2008) Robot social presence and gender: Do females view robots differently than males? In: 2008 3rd ACM/IEEE international conference on human–robot interaction (HRI), pp 263–270

  60. Landrum AR, Eaves BS, Shafto P (2015) Learning to trust and trusting to learn: a theoretical framework. Trends Cogn Sci 19:109–111. https://doi.org/10.1016/j.tics.2014.12.007

    Article  Google Scholar 

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Acknowledgments

We would like to thank Dr. Tatsuya Nomura for permission to use the NARS and for his advice in translating the scale. We also thank Idil Franko, Orhun Uluşahin, Seref Can Esmer, and Mine Yürekli for their contribution as research assistants.

Funding

This research was funded by the EC H2020 L2TOR Project (Grant 688014).

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Correspondence to Junko Kanero.

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Appendix 1

Appendix 1

The Turkish version of the Negative Attitudes toward Robots Scale (NARS; [43]) used in the present study. The back-translation of the Turkish items are indicated in parentheses (the back-translation is different from the original English version of the NARS). The subscales are: S1 (negative attitudes toward interacting with robots), S2 (negative attitudes toward the social influence of robots), and S3 (negative attitude toward emotions involved in the interaction with robots). In this study, the whole survey was administered, but S3 was dropped from analyses as it did not highly correlate with S1 and S2.

Subscale

Item

S2

Eğer robotların kendi duyguları olursa kaygılı hissederim

(I will feel anxious if robots have their own emotions.)

S2

Robotların insanlara daha çok benzemesinin insanoğlu açısından olumsuz bir sonucu olacağını düşünüyorum

(I surmise that there will be negative consequences for humans when robots become more similar to humans.)

S3

Robotlarla etkileşime girersem kendimi rahat hissederim

(I will feel comfortable if I interact with robots.)

S1

Robotların kullanıldığı bir iş yerinde çalıştığımı hayal ettiğimde kaygılı hissederim

(I feel anxiety when I imagine that I may be employed or assigned to a workplace where robots are used.)

S3

Eğer robotların kendi duyguları olursa kendimi onlara yakın hissederim

(I will feel close to robots if they have their own emotions.)

S3

Robotların duygusal davrandıklarını gördüğümde kendimi daha rahat hissederim

(I feel more comfortable when I see robots behaving affectively.)

S1

Robotlar hakkında bir şey duyduğumda bile kendimi çaresiz hissediyorum

(I feel helpless even by hearing something about robots.)

S1

Başkalarının önünde robot kullanacak olursam kendimi utandırabilirim

(I am likely to be embarrassed when I use robots in public.)

S1

“Yapay zekanın verdiği kararlar” veya “robotların verdiği kararlar” gibi ifadeler beni rahatsız ediyor

(The words “artificial intelligence” or “decision by robots” make me feel unpleasant.)

S1

Sadece robotların önünde durmak bile bende gerginlik yaratır

(Even standing in front of robots will strain me.)

S2

Robotlara aşırı bağlı olmak gelecekte olumsuzluğa sebep olabilir

(I surmise that becoming extremely dependent on robots will have negative consequences for humans in the future.)

S1

Robotlarla etkileşime girersem kendimi tedirgin hissederim

(I will feel nervous if I interact with robots.)

S2

Robotların çocukların zihnini olumsuz yönde etkileyeceklerinden korkuyorum

(I am afraid that robots may negatively influence children’s minds.)

S2

Gelecekteki toplumlara robotların hükmedeceği kanısındayım

(I surmise that robots may dominate future societies.)

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Kanero, J., Oranç, C., Koşkulu, S. et al. Are Tutor Robots for Everyone? The Influence of Attitudes, Anxiety, and Personality on Robot-Led Language Learning. Int J of Soc Robotics 14, 297–312 (2022). https://doi.org/10.1007/s12369-021-00789-3

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