skip to main content
10.1145/2858036.2858351acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Effects of Pedagogical Agent's Personality and Emotional Feedback Strategy on Chinese Students' Learning Experiences and Performance: A Study Based on Virtual Tai Chi Training Studio

Published:07 May 2016Publication History

ABSTRACT

In virtual learning environment, both personality and emotional features of animated pedagogical agents (APAs) may influence learning. To investigate this question, we developed four APAs with two distinct personality types and two sets of gestures expressing distinct emotional feedback. Effects of APAs' personality types and emotional feedback strategies on learning experiences and performance were assessed experimentally using a virtual Tai Chi training system. Fifty six participants completed the experiment. Results showed that positive emotional feedback strategy led to better learning experiences and performance than negative feedback strategy. Moreover, personality type had significant effect on learning. Choleric APAs led to better performance than Phlegmatic APAs. Personality types moderated the effect of emotional feedback on learning satisfaction. Our study demonstrates that APAs with distinct personality types and emotional feedback are important design parameters for virtual learning environments.

Skip Supplemental Material Section

Supplemental Material

References

  1. Albright, L., Kenny, D. A., and Malloy, T. E. Consensus in Personality Judgments at Zero Acquaintance. Journal of Personality and Social Psychology 55, (1988), 387--395.Google ScholarGoogle ScholarCross RefCross Ref
  2. Allbeck, J., and Badler, N. Toward Representing Agent Behaviors Modified by Personality and Emotion. Embodied Conversational Agents at AAMAS2, (2002), 15--19.Google ScholarGoogle Scholar
  3. Arellano, B. D., Varona, J., and Perales, F. J. Generation and Visualization of Emotional States in Virtual Characters. Comp. Anim. Virtual Worlds 19, (July, 2008), 259--270. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Atkinson, R. K., Mayer, R. E., and Merrill, M. M. Fostering Social Agency in Multimedia Learning: Examining the Impact of an Animated Agent's Voice. Contemporary Educational Psychology 30, 1(2005), 117--139.Google ScholarGoogle ScholarCross RefCross Ref
  5. Balomenos, T., Raouzaiou, A., Ioannou, S., Drosopoulos, A., Karpouzis, K., and Kollias. S. Emotion Analysis in Man-Machine Interaction Systems," Proc. Workshop Machine Learning for Multimodal Interaction, (2004), 318--328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Baylor, A. L., and Kim, Y. Simulating Instructional Roles through Pedagogical Agents. International Journal of Artificial Intelligence in Education 15, (2005), 95--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Beale, R., and Creed, C. Affective Interaction: How Emotional Agents Affect Users. International Journal of Human Computer Studies 67, (2009), 755--776. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Borkenau, P., and Liebler, A. Trait Inferences: Sources of Validity at Zero-acquaintance. Journal of Personality and Social Psychology 62, (1992), 645657.Google ScholarGoogle ScholarCross RefCross Ref
  9. Borkenau, P., Mauer, N., Riemann, R., Spinath, F. M., and Angleitner, A. Thin Slices of Behavior as Cues of Personality and Intelligence. Journal of Personality and Social Psychology 86, 4 (2004), 599--614.Google ScholarGoogle ScholarCross RefCross Ref
  10. Burleson, W., and Picard, R. Gender-specific Approaches to Developing Emotionally Intelligent Learning Companions. Intelligent Systems 22, 4 (2007), 62--69. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Chee, B. T. T., Taezoon, P., Xu, Q., Ng, J., and Tan, O. Personality of Social Robots Perceived through the Appearance. Work 41, 1 (2012), 272--276.Google ScholarGoogle ScholarCross RefCross Ref
  12. Csikszentmihalyi, M. Flow: The psychology of optimal experience. NY: Harper and Row, 1990.Google ScholarGoogle Scholar
  13. Dekker T. W. G. Personality in Embodied Conversational Agents?: Effects on User Experience, In Proc. of 17th Twente Student Conference on IT (2012).Google ScholarGoogle Scholar
  14. Devine, P. G. Stereotypes and Prejudice: Their Automatic and Controlled Components. Journal of Personality and Social Psychology 56, 2 (1989), 5--18.Google ScholarGoogle ScholarCross RefCross Ref
  15. D'Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., et al. Auto Tutor Detects and Responds to Learners Affective and Cognitive States. In Proc. of International conference on intelligent tutoring systems (2008).Google ScholarGoogle Scholar
  16. Dunsworth, Q. and Atkinson, R. K. Fostering Multimedia Learning of Science: Exploring the Role of an Animated Agent's Image. Computers and Education 49, 3 (2007), 677--690. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Engeser, S. Advances in Flow Research. S. Engeser, Ed. 2012.Google ScholarGoogle Scholar
  18. Epleya, N. A and Krugerb, J. When What you Type isn't What They Read: The Perseverance of Stereotypes and Expectancies over E-mail. Journal of Experimental Social Psychology 41, 4 (2005), 414--422.Google ScholarGoogle Scholar
  19. Eysenck, H. J. and Eysenck, M. W. Personality and Individual Differences, NY: plenum, 1985.Google ScholarGoogle ScholarCross RefCross Ref
  20. Farabee, D., Nelson, R., and Spence, R. Psychosocial Profiles of Criminal Justice- and Non-Criminal Justice Referred Clients in Treatment. Criminal Justice and Behaviour 20, (1993), 336--346.Google ScholarGoogle ScholarCross RefCross Ref
  21. Fink, B., Neave N., Manning, J. T., and Grammer, K. Facial Symmetry and the 'big-five' Personality Factors. Pers. Individ. Dif. 39, 3(Aug. 2005), 523--529.Google ScholarGoogle ScholarCross RefCross Ref
  22. Forgas J.P., ed, Feeling and Thinking: The Role of Affect in Social Cognition. Cambridge University Press, Cambridge, UK, 2000.Google ScholarGoogle Scholar
  23. Freedman, N. Hands, Words and Mind: On the Structuralization of Body Movements during Discourse and the Capacity for Verbal Representation. Communicative Structures and Psychic Structures: A Psychoanalytic Interpretation of Communication 1, (1977), 109--132.Google ScholarGoogle ScholarCross RefCross Ref
  24. Gelder, B. D. Towards the Neurobiology of Emotional Body Language. Nature Reiview Neuroscience 7, 3 (2006), 242- 249.Google ScholarGoogle Scholar
  25. Glowinski, D., Dael, N., Camurri, a., Volpe, G., Mortillaro, M., and Scherer, K. Toward a Minimal Representation of Affective Gestures. IEEE Transactions on Affective Computing2, 2 (2011), 106--118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Howarth, E., and Zumbo, B. D. An Empirical Investigation of Eysenck's typology. Journal of Research in Personality 23, 3 (1989), 343--353.Google ScholarGoogle ScholarCross RefCross Ref
  27. Isbister, K., and Nass, C. Consistency of Personality in Interactive Characters: Verbal Cues, Non-verbal cues, and User Characteristics. International Journal of Human Computer Studies 53, 2 (2000), 251--267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Kenny, D. A., Horner, C., Kashy, D. A., and Chu, L. Consensus at Zero-acquaintance: Replication, Behavioral Cues, and Stability. Journal of Personality and Social Psychology 62, (1992), 88--97.Google ScholarGoogle ScholarCross RefCross Ref
  29. Kim, C. The Role of Affective and Motivational Factors in Designing Personalized Learning Environments. Educational Technology Research and Development, 60, 4 (2012), 563--584.Google ScholarGoogle Scholar
  30. Kim, Y., Baylor, A. L. and Shen, E. Pedagogical Agents as Learning Companions: The Impact of Agent Emotion and Gender. J. Comput. Assist. Learn 23, 3 (2007), 220--234.Google ScholarGoogle Scholar
  31. Kim, Y., and Baylor, A. L. A Social-cognitive Framework for Pedagogical Agents as Learning Companions. Educational Technology Research and Development 5, 4(2006), 569--590.Google ScholarGoogle Scholar
  32. Kim, Y., and Wei, Q. The Impact of Learner Attributes and Learner Choice in an Agent-based Environment. Computers and Education56, 2 (2011), 505--514. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. La France, B., Heisel, A. and Beatty, M. Is There Empirical Evidence for a Nonverbal Profile of Extraversion? A Meta Analysis and Critique of the Literature. Comm. Monographs 71, (2004), 28--48.Google ScholarGoogle ScholarCross RefCross Ref
  34. Lee, K.M., et al. Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human-Robot Interaction. Journal of Communication 56, (2006), 754--772.Google ScholarGoogle ScholarCross RefCross Ref
  35. Lin, L., Atkinson, R. K., Christopherson, R. M., Joseph, S. S., and Harrison, C. J. Animated Agents and Learning: Does the Type of Verbal Feedback They Provide Matter? Computers and Education 67, (2013), 239--249.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Meijer, M. The Contribution of General Features of Body Movement to the Attribution of Emotions. Journal of Nonverbal Behavior 13, 4 (1989), 247--268.Google ScholarGoogle ScholarCross RefCross Ref
  37. McRorie, M., Sneddon, I., McKeown, G., Bevacqua, E., de Sevin, E., and Pelachaud, C. Evaluation of Four Designed Virtual Agent Personalities. IEEE Transactions on Affective Computing 3, 3 (2012), 311--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Nass, C. and Lee, K.M. Does computer-generated Speech Manifest Personality? Experimental Test of Recognition, Similarity-attraction, and Consistence attraction. Journal of Exeprimental Psychology: Applied 7, (2001), 171--181.Google ScholarGoogle ScholarCross RefCross Ref
  39. Naumann, L. P., Vazire, S., Rentfrow, P. J., and Gosling, S. D. Personality Judgments based on Physical Appearance. Personality and Social Psychology Bulletin 35, 12 (2009), 1661--1671.Google ScholarGoogle ScholarCross RefCross Ref
  40. Paunonen, S. V, Lefave, S., and Goldberg, H. (n.d.). Facial Features as Personality Cues, Journal of Personality 67, 3 (June 1999), 555--583.Google ScholarGoogle ScholarCross RefCross Ref
  41. Qian, M. Y., Wu, G. C., Zhu, R. C., and Zhang, S. Development of the Revised Eysenck Personality Questionaire Short Scale for Chinese (EPQ-RSC). Acta Psychologica Sinica 32, 3(2000), 317--323.Google ScholarGoogle Scholar
  42. Rázuri, J. G., Larsson, A., Rahmani, R., Sundgren, D., Bonet, I., and Moran, A. Recognition of Emotions by the Emotional Feedback through Behavioral Human Poses. International Journal of Computer Science Issues 12, 1 (2015), 7--17.Google ScholarGoogle Scholar
  43. Read, S. J., and Miller, L. C. Virtual Personalities: A Neural Network Model of Personality. Personality and Social Psychology Review 6, 4 (2002), 357--369.Google ScholarGoogle Scholar
  44. Reeves, B., and Nass, C. I. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Computers and mathematics with applications 33, 5 (1997), 128.Google ScholarGoogle Scholar
  45. Robison, J., McQuiggan, S., and Lester, J. Developing Empirically Based Student Personality Profiles for Affective Feedback Models. In Proceedings of the tenth international conference on intelligent tutoring systems (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Saggino, A. The Big Three or the Big Five? A Replication Study. Personality and Individual Differences 28, 5(2000), 879--886.Google ScholarGoogle ScholarCross RefCross Ref
  47. Sträfling, N., Fleischer, I., Polzer, C., Leutner, D., and Krämer, N. C. Teaching Learning Strategies with a Pedagogical Agent. Journal of Media Psychology: Theories, Methods, and Applications 22, 2 (2010), 73--83.Google ScholarGoogle Scholar
  48. Shi, B., Hu, Y. G., and Shi, F. A Study on the Appraisal Criteria of University Students' 24-stroke Taiji. Journal of Nanyang Teachers' College (Natural Sciences Edition) 2, 3 (2003), 3--5.Google ScholarGoogle Scholar
  49. Shiffrar, M., and Pinto, J. The Visual Analysis of Bodily Motion, Common mechanisms in perception and action: Attention and Performance, in Prinz, W., Hommel, B. (eds.) Oxford, Oxford University Press (2002), 381--399.Google ScholarGoogle Scholar
  50. Sweetser, P., and Wyeth, P. GameFlow: A Model for Evaluating Player Enjoyment in Games. ACM Computers in Entertainment 3, 3 (2005), 1--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Terzis, V., Moridis, C. N., and Economides, A. a. The Effect of Emotional Feedback on Behavioral Intention to Use Computer Based Assessment. Computers and Education 59, 2 (2012), 710--721. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Tipples, J. Wide Eyes and an Open Mouth Enhance Facial Threat. Cognition and Emotion 21, (2007), 535557.Google ScholarGoogle ScholarCross RefCross Ref
  53. Trevino, L. K., and Webster, J. Flow in Computer Mediated Communication: Electronic Mail and Voice Mail Evaluation and Impacts. Communication Research 19, 5 (1992), 539--573.Google ScholarGoogle ScholarCross RefCross Ref
  54. Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., and Collins, H. The Politeness Effect: Pedagogical Agents and Learning Outcomes. International Journal of Human-Computer Studies 66, 2 (2008), 98--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Walline, J. High Eye-Q. Science 320, 5879 (2008), 993.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
    May 2016
    6108 pages
    ISBN:9781450333627
    DOI:10.1145/2858036

    Copyright © 2016 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 7 May 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    CHI '16 Paper Acceptance Rate565of2,435submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

    Upcoming Conference

    CHI '24
    CHI Conference on Human Factors in Computing Systems
    May 11 - 16, 2024
    Honolulu , HI , USA

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader