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Towards Adaptive Robotic Tutors in Universities: A Field Study

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Persuasive Technology (PERSUASIVE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12684))

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Abstract

Learning in university setting includes the challenging task of self-motivation of the learner. The use of social robots has been shown to support the learner in their social learning process. In this paper, we address the motivation of learners in terms of self-determination theory as a theoretical framework to address need satisfaction. To this end, we conducted a field study using an adaptive robotic tutor that supports learners in exam preparation using an online learning session. With the aim to not only benefit motivation, but also academic success, we draw from research in social robotics in education as well as from adaptive tutoring, to create an adequate learning scenario. Adaptation is realized by a simple content and learner model and resulted in a significantly higher perceived use of the tutoring compared to a control condition. Our results also showed descriptive benefits such as increased perceived tutor quality, need satisfaction and motivation resulting from the adaptive tutoring. Finally, we found significantly better exam performance with the robotic tutor in the adaptive or non-adaptive version relative to students not participating in the robotic tutoring.

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Notes

  1. 1.

    Nao robot: https://www.softbankrobotics.com/emea/en/nao.

  2. 2.

    Zoom: https://zoom.us/.

References

  1. Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1(1), 71–81 (2009)

    Article  Google Scholar 

  2. Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)

    Google Scholar 

  3. Breazeal, C., Dautenhahn, K., Kanda, T.: Social robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1935–1972. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_72

    Chapter  Google Scholar 

  4. Deci, E.L., Ryan, R.M.: The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol. Inquiry 11(4), 227–268 (2000)

    Article  Google Scholar 

  5. Deci, E.L., Ryan, R.M., Williams, G.C.: Need satisfaction and the self-regulation of learning. Learn. Individ. Differences 8(3), 165–183 (1996)

    Article  Google Scholar 

  6. Deci, E.L., Vallerand, R.J., Pelletier, L.G., Ryan, R.M.: Motivation and education: the self-determination perspective. Educ. Psychol. 26(3–4), 325–346 (1991)

    Article  Google Scholar 

  7. Deublein, A., Pfeifer, A., Merbach, K., Bruckner, K., Mengelkamp, C., Lugrin, B.: Scaffolding of motivation in learning using a social robot. Comput. Educ. 100(125), 182–190 (2018)

    Article  Google Scholar 

  8. Donnermann, M., Schaper, P., Lugrin, B.: Integrating a social robot in higher education - a field study. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 573–579. IEEE (2020)

    Google Scholar 

  9. Guay, F., Vallerand, R.J., Blanchard, C.: On the assessment of situational intrinsic and extrinsic motivation: the situational motivation scale (sims). Motiv. Emot. 24(3), 175–213 (2000)

    Article  Google Scholar 

  10. Kennedy, J., Baxter, P., Belpaeme, T.: The robot who tried too hard: social behaviour of a robot tutor can negatively affect child learning. In: 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 67–74. IEEE (2015)

    Google Scholar 

  11. Leyzberg, D., Ramachandran, A., Scassellati, B.: The effect of personalization in longer-term robot tutoring. ACM Trans. Hum.-Robot Interact. (THRI) 7(3), 1–19 (2018)

    Article  Google Scholar 

  12. Leyzberg, D., Spaulding, S., Scassellati, B.: Personalizing robot tutors to individuals’ learning differences. In: 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 423–430. IEEE (2014)

    Google Scholar 

  13. Leyzberg, D., Spaulding, S., Toneva, M., Scassellati, B.: The physical presence of a robot tutor increases cognitive learning gains. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 34 (2012)

    Google Scholar 

  14. Li, J.: The benefit of being physically present: a survey of experimental works comparing copresent robots, telepresent robots and virtual agents. Int. J. Hum.-Comput. Stud. 77, 23–37 (2015)

    Article  Google Scholar 

  15. Pfeifer, A., Lugrin, B.: Female robots as role-models? - the influence of robot gender and learning materials on learning success. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 276–280. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_51

    Chapter  Google Scholar 

  16. Phobun, P., Vicheanpanya, J.: Adaptive intelligent tutoring systems for e-learning systems. Proc.-Soc. Behav. Sci. 2(2), 4064–4069 (2010)

    Article  Google Scholar 

  17. Rosenberg-Kima, R., Koren, Y., Yachini, M., Gordon, G.: Human-robot-collaboration (hrc): Social robots as teaching assistants for training activities in small groups. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 522–523, March 2019. https://doi.org/10.1109/HRI.2019.8673103

  18. Ryan, R.M., Deci, E.L.: Self-determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. Guilford Publications (2017)

    Google Scholar 

  19. Ryan, R.M., Mims, V., Koestner, R.: Relation of reward contingency and interpersonal context to intrinsic motivation: a review and test using cognitive evaluation theory. J. Pers. Soc. Psychol. 45(4), 736 (1983)

    Article  Google Scholar 

  20. Saerbeck, M., Schut, T., Bartneck, C., Janse, M.D.: Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1613–1622 (2010)

    Google Scholar 

  21. Schodde, T., Bergmann, K., Kopp, S.: 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, pp. 128–136 (2017)

    Google Scholar 

  22. Sheldon, K.M., Filak, V.: Manipulating autonomy, competence, and relatedness support in a game-learning context: new evidence that all three needs matter. Br. J. Soc. Psychol. 47(2), 267–283 (2008)

    Article  Google Scholar 

  23. Teo, T.: Development and validation of the e-learning acceptance measure (ELAM). Internet High. Educ. 13(3), 148–152 (2010)

    Article  Google Scholar 

  24. Tiberius, R.G., Billson, J.M.: The social context of teaching and learning. New Directions Teach. Learn. 1991(45), 67–86 (1991)

    Article  Google Scholar 

  25. Velez, J.J., Cano, J.: The relationship between teacher immediacy and student motivation. J. Agric. Educ. 49(3), 76–86 (2008)

    Article  Google Scholar 

  26. Witt, P.L., Wheeless, L.R., Allen, M.: A meta-analytical review of the relationship between teacher immediacy and student learning. Commun. Monogr. 71(2), 184–207 (2004)

    Article  Google Scholar 

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Correspondence to Melissa Donnermann .

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Donnermann, M., Schaper, P., Lugrin, B. (2021). Towards Adaptive Robotic Tutors in Universities: A Field Study. In: Ali, R., Lugrin, B., Charles, F. (eds) Persuasive Technology. PERSUASIVE 2021. Lecture Notes in Computer Science(), vol 12684. Springer, Cham. https://doi.org/10.1007/978-3-030-79460-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-79460-6_3

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