Social robots are increasingly being adopted as companions in educational scenarios. Self-efficacy, a viable construct for comprehending performance, particularly on academic tasks, has lately received great attention. In this study, participants completed four sections of the Wisconsin Card-Sorting Task (WCST) with a social robot Kebbi. The robot performed four kinds of expressions consisting of different combinations of Laban-theory-based motion with a positive voice designed to point out the mistakes the participant made. The impressions of the robot were reported in the post-experimental questionnaires while the bio-signals of the participant including heart rate and brainwave were collected by wearable devices. The results demonstrated that the participants tended to find the robot with the designed motion more likable, and they were less likely to feel frustrated and experienced lower levels of stress when the robot communicated with motion and voice simultaneously.

1. [1] T. Maier, S. Abdullah, C. McComb, and J. Menold, “A Query Conundrum: The Mental Challenges of Using a Cognitive Assistant,” SN Computer Science, Vol.2, No.3, pp. 1-15, 2021.
2. [2] A. R. Anthony, “Academic self-efficacy: from educational theory to instructional practice,” Perspectives on Medical Education, Vol.1, No.2, pp. 76-85, 2012.
3. [3] H. P. Phan, “Interrelations between self-efficacy and learning approaches: A developmental approach,” Educational Psychology, Vol.31, No.2, pp. 225-246, 2011.
4. [4] B. J. Fogg, “Persuasive computers: perspectives and research directions,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 225-232, 1998.
5. [5] B. J. Fogg, “Captology: the study of computers as persuasive technologies,” CHI 98 Conf. Summary on Human Factors in Computing Systems, p. 385, 1998.
6. [6] S. Zafari, I. Schwaninger, M. Hirschmanner, C. Schmidbauer, A. Weiss, and S. T. Koeszegi, “‘You Are Doing so Great!’ – The Effect of a Robot’s Interaction Style on Self-Efficacy in HRI,” Proc. of the 2019 28th IEEE Int. Conf. on Robot and Human Interactive Communication (RO-MAN), pp. 1-7, 2019.
7. [7] E. S. Cross, R. Hortensius, and A. Wykowska, “From social brains to social robots: applying neurocognitive insights to human-robot interaction,” Philosophical Trans. of the Royal Society B, Vol.374, No.1771, 20180024, 2019.
8. [8] M. Tielman, M. Neerincx, J.-J. Meyer, and R. Looije, “Adaptive emotional expression in robot-child interaction,” Proc. of the 2014 9th ACM/IEEE Int. Conf. on human-robot Interaction (HRI), pp. 407-414, 2014.
9. [9] Y. Li, H. Sekino, E. Sato-Shimokawara, and T. Yamaguchi, “Study of Combining Laban-Theory-Based Motion with Frankl Psychology for Partner Robots,” Proc. of the 2021 Symp. on Fuzzy, Artificial Intelligence, Neural Networks and Computational Intelligence (FAN2021), pp. 57-61, 2021.
10. [10] W.-F. Hsieh, Y. Li, E. Sato-Shimokawara, and T. Yamaguchi, “Analyzing Individual Different Perception of a Communication Robot Physical Attribute,” The 29th Symp. on Fuzzy, Artificial Intelligence, Neural Networks and Computational Intelligence, pp. 237-241, 2019.
11. [11] C. Bartneck, T. Suzuki, T. Kanda, and T. Nomura, “The influence of people’s culture and prior experiences with Aibo on their attitude towards robots,” Ai & Society, Vol.21, No.1, pp. 217-230, 2007.
12. [12] L. P. Robert, “Personality in the Human Robot Interaction Literature: A Review and Brief Critique,” Proc. of the 24th Americas Conf. on Information Systems, pp. 16-18, 2018.
13. [13] J. H. Ko, O. Monchi, A. Ptito, M. Petrides, and A. P. Strafella, “Repetitive transcranial magnetic stimulation of dorsolateral prefrontal cortex affects performance of the Wisconsin card sorting task during provision of feedback,” Int. J. of Biomedical Imaging, Vol.2008, 143238, 2008.
14. [14] T. Nomura, “Humans’ Subjective Evaluation in Human-Agent Interaction (HAI),” J. of the Japanese Society for Artificial Intelligence, Vol.31, No.2, pp. 224-229, 2016.
15. [15] S. A. Woods and S. E. Hampson, “Measuring the Big Five with single items using a bipolar response scale,” European J. of Personality, Vol.19, No.5, pp. 373-390, 2005.
16. [16] J. Szente, “Empowering young children for success in school and in life,” Early Childhood Education J., Vol.34, No.6, pp. 449-453, 2007.
17. [17] Y. Cheng, “Academic self-efficacy and assessment,” Educational Psychology, Vol.40, No.4, pp. 389-391, 2020.
18. [18] A. J. Elliot, C. S. Dweck, and D. S. Yeager (Eds.), “Handbook of Competence and Motivation,” Guilford Press, 2013.
19. [19] A. Bandura, W. H. Freeman, and R. Lightsey, “Self-efficacy: The exercise of control,” J. of Cognitive Psychotherapy, Vol.13, pp. 158-166, 1999.
20. [20] A. Bandura, “Guide for constructing self-efficacy scales,” Self-Efficacy Beliefs of Adolescents, Vol.5, No.1, pp. 307-337, 2006.
21. [21] F. Salili, C. Chiu, and S. Lai, “The influence of culture and context on students’ motivational orientation and performance,” Student Motivation, pp. 221-247, 2001.
22. [22] D. Ahlgren and I. M. Verner, “Building Self Efficacy in Robotics Education,” Proc. of the 2007 Annual Conf. & Exposition, pp. 18-27, 2007.
23. [23] S. Saunderson and G. Nejat, “How robots influence humans: A survey of nonverbal communication in social human-robot interaction,” Int. J. of Social Robotics, Vol.11, No.4, pp. 575-608, 2019.
24. [24] N. Mavridis, “A review of verbal and non-verbal human-robot interactive communication,” Robotics and Autonomous Systems, Vol.63, pp. 22-35, 2015.
25. [25] C. L. Sidner, C. Lee, C. D. Kidd, N. Lesh, and C. Rich, “Explorations in engagement for humans and robots,” Artificial Intelligence, Vol.166, Nos.1-2, pp. 140-164, 2005.
26. [26] C. Darwin and P. Phillip, “The expression of the emotions in man and animals,” Oxford University Press, 1998.
27. [27] N. L. Robinson, T.-N. Hicks, G. Suddrey, and D. J. Kavanagh, “The robot self-efficacy scale: Robot self-efficacy, likability and willingness to interact increases after a robot-delivered tutorial,” Proc. of the 2020 29th IEEE Int. Conf. on Robot and Human Interactive Communication (RO-MAN), pp. 272-277, 2020.
28. [28] J. M. Kory-Westlund and C. Breazeal, “Exploring the effects of a social robot’s speech entrainment and backstory on young children’s emotion, rapport, relationship, and learning,” Frontiers in Robotics and AI, Vol.6, p. 54, 2019.
29. [29] R. Looije, M. A. Neerincx, and F. Cnossen, “Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors,” Int. J. of Human-Computer Studies, Vol.68, No.6, pp. 386-397, 2010.
30. [30] S. Ono, J. Kusaka, T. Obo, and N. Kubota, “Exercise support system with robot partner based on feeling of self-efficacy,” Proc. of the 2014 Int. Symp. on Micro-NanoMechatronics and Human Science (MHS), pp. 1-5, 2014.
31. [31] J. K. Burgoon, V. Manusov, and L. K. Guerrero, “Nonverbal communication,” Routledge, 2021.
32. [32] A. G. Brooks and R. C. Arkin, “Behavioral overlays for non-verbal communication expression on a humanoid robot,” Autonomous robots, Vol.22, No.1, pp. 55-74, 2007.
33. [33] M. L. Walters, D. S. Syrdal, K. Dautenhahn, R. T. Boekhorst, and K. L. Koay, “Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion,” Autonomous Robots, Vol.24, No.2, pp. 159-178, 2008.
34. [34] M. Axelsson, I. P. Bodala, and H. Gunes, “Participatory Design of a Robotic Mental Well-Being Coach,” Proc. of the 2021 30th IEEE Int. Conf. on Robot and Human Interactive Communication (RO-MAN), pp. 1081-1088, 2021.
35. [35] J. A. Levy and M. P. Duke, “The Use of Laban Movement Analysis in the Study of Personality, Emotional State and Movement Style: An Exploratory Investigation of the Veridicality of “Body Language”,” Individual Differences Research, Vol.1, No.1, pp. 39-63, 2003.
36. [36] S. C. Koch, “Basic principles of movement analysis: Steps toward validation of the KMP,” S. C. Koch and S. Bender (Eds.), “Movement Analysis,” Logos Berlin, 2007.
37. [37] T. Shafir, R. P. Tsachor, and K. B. Welch, “Emotion regulation through movement: unique sets of movement characteristics are associated with and enhance basic emotions,” Frontiers in Psychology, Vol.6, p. 2030, 2016.
38. [38] J. Rett and J. Dias, “Human-robot interface with anticipatory characteristics based on Laban Movement Analysis and Bayesian models,” Proc. of the 2007 IEEE 10th Int. Conf. on Rehabilitation Robotics, pp. 257-268, 2007.
39. [39] M. Masuda, S. Kato, and H. Itoh, “A Laban-based approach to emotional motion rendering for human-robot interaction,” Proc. of the Int. Conf. on Entertainment Computing, pp. 372-380, 2010.
40. [40] A. Bacula and A. LaViers, “Character recognition on a humanoid robotic platform via a laban movement analysis,” Proc. of the 5th Int. Conf. on Movement and Computing, pp. 1-8, 2018.
41. [41] M. Masuda, S. Kato, and H. Itoh, “Laban’s Feature Value Set and Emotion Estimation from Body Motion of Human Form Robot Based on Laban Movement Analysis,” J. of Japan Society of Kansei Engineering, Vol.10, No.2, pp. 295-303, 2011.
42. [42] J. Abrossimoff, A. Pitti, and P. Gaussier, “Working-memory prefrontal model for cognitive flexibility in task-switching and selection,” Proc. of the 2020 Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-7, 2020.
43. [43] Y. Li, E. Sato-Shimokawara, and T. Yamaguchi, “The Influence of Robot’s Unexpected Behavior on Individual Cognitive Performance,” Proc. of the 30th IEEE Int. Conf. on Robot and Human Interactive Communication (RO-MAN 2021), 2021.
44. [44] Y. Li, E. Sato-Shimokawara, and T. Yamaguchi, “Investigation of Perception towards Robot Expressions Considering Attitude and Personality,” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.33, No.4, pp. 501-510, 2021.
45. [45] R. R. McCrae, P. T. Costa Jr., and T. A. Martin, “The NEO-PI-3: A more readable revised NEO personality inventory,” J. of Personality Assessment, Vol.84, No.3, pp. 261-270, 2005.
46. [46] pulsesensor. https://pulsesensor.com/ [accessed December 13, 2021]
47. [47] K. Permana, S. K. Wijaya, and P. PrajitNo, “Controlled wheelchair based on brain computer interface using Neurosky Mindwave Mobile 2,” AIP Conf. Proc., Vol.2168, No.1, 020022, 2019.
48. [48] Google Forms. https://docs.google.com/forms/ [accessed December 13, 2021]
49. [49] D. S. Syrdal, K. Dautenhahn, K. L. Koay, M. L. Walters, and W. C. Ho, “Sharing spaces, sharing lives – The impact of robot mobility on user perception of a home companion robot,” Int. Conf. on Social Robotics, pp. 321-330, 2013.
50. [50] E. Guizzo, “How aldebaran robotics built its friendly humanoid robot, Pepper,” IEEE Spectrum, 2014.
51. [51] L. Shu, Y. Yu, W. Chen, H. Hua, Q. Li, J. Jin, and X. Xu, “Wearable emotion recognition using heart rate data from a smart bracelet,” Sensors, Vol.20, No.3, p. 718, 2020.
52. [52] L. Desideri, C. Ottaviani, M. Malavasi, R. Marzio, and P. Bonifacci, “Emotional processes in human-robot interaction during brief cognitive testing,” Computers in Human Behavior, Vol.90, pp. 331-342, 2019.
53. [53] H. Murakami and H. Ohira, “Influence of attention manipulation on emotion and autonomic responses,” Perceptual and Motor Skills, Vol.105, No.1, pp. 299-308, 2007.
54. [54] A. J. Camm, M. Malik, J. T. Bigger et al., “Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,” Circulation, pp. 1043-1065, 1996.
55. [55] S. Vaezi and N. Fallah, “The relationship between self-efficacy and stress among Iranian EFL teachers,” J. of Language Teaching and Research, Vol.2, No.5, p. 1168, 2011.
56. [56] M. Vazquez-Marrufo, E. Vaquero, M. J. Cardoso, and C. M. Gomez, “Temporal evolution of α and β bands during visual spatial attention,” Cognitive Brain Research, Vol.12, No.2, pp. 315-320, 2001.