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
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).
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.
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
Ortega L (2009) Understanding second language acquisition. Routledge, Taylor & Francis Group, London
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
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
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
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
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
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
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
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
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
Chamorro-Premuzic T, von Stumm S, Furnham A (2015) The Wiley-Blackwell handbook of individual differences. Wiley-Blackwell, Malden, MA
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
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
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
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
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
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
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
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
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
Robert L (2018) Personality in the human robot interaction literature: a review and brief critique. Social Science Research Network, Rochester, NY
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
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
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
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
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
Liu J (2008) L1 use in L2 vocabulary learning: facilitator or barrier. Int Educ Stud 1:65–69
Horwitz EK, Horwitz MB, Cope J (1986) Foreign language classroom anxiety. Mod Lang J 70:125. https://doi.org/10.2307/327317
Horwitz E (2001) Language anxiety and achievement. Annu Rev Appl Linguist 21:112–126. https://doi.org/10.1017/S0267190501000071
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
Ożańska-Ponikwia K (2017) Extraverts and introverts in the FL classroom setting. In: Second language learning and teaching, pp 93–105
Dornyei Z, Ryan S (2015) The psychology of the language learner revisited. Routledge, New York, NY
Dewaele J-M (2012) Personality in second language acquisition. In: The encyclopedia of applied linguistics. American Cancer Society
Naiman N (1978) The good language learner: a report. Ontario Institute for Studies in Education, Toronto
Ehrman M (2008) Personality and good language learners. In: Griffiths C (ed) Lessons from good language learners. Cambridge University Press, Cambridge, pp 61–72
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
Ellis R (2004) Individual differences in second language learning. In: The handbook of applied linguistics. Blackwell, Malden, MA, pp 525–551
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
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
Wakamoto N (2009) Extroversion/introversion in foreign language learning: interactions with learner strategy use. Peter Lang, Bern
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
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
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
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
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
Dunn LM, Dunn DM (2007) Peabody picture vocabulary test—fourth edition (PPVT). NCS Pearson, Minneapolis, MN
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
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
R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
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
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
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
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
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
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
Clenton J, Booth P (2021) Vocabulary and the four skills: pedagogy, practice, and implications for teaching vocabulary. Routledge, Abingdon
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
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
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
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
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.
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This research was funded by the EC H2020 L2TOR Project (Grant 688014).
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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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DOI: https://doi.org/10.1007/s12369-021-00789-3