Different realism designs of 2D virtual agents and its

Ahmad Zamzuri Bin Mohamad Ali

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Type :	article
Subject :	L Education (General)
Main Author :	Ahmad Zamzuri Bin Mohamad Ali
Additional Authors :	Vicneas Muniady
Title :	Different realism designs of 2D virtual agents and its

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

This research was aimed to analyse students’ emotions caused by the character’s realism design in the dimension of arousal and its’ effect on emotions in learning. Thus, 2D female virtual agents with four different realism appearance namely realistic, semi-realistic, stylized and cartoon-like were designed and developed. Thus, quasi experimental design was used to answer the research questions derived from this study. The data obtained from the experiment was analysed using ANOVA, post hoc and bootstrap mediation analysis. A number of 600 Electrical Engineering students were chosen from seven polytechnics in Malaysia as respondents in the experiment. From the research it was found that cartoon-like design obtained the lowest mean score arousal followed by stylized, realistic and semi-realistic designs. However, cartoon-like design scored highest mean score for emotions in learning followed by semi-realistic, realistic and stylized designs. Therefore, cartoon-like agent is the best agent among others in inducing positive emotions in learning. In addition, the mediation analysis shows that arousal has mediating effects on relation between different realism designs and emotions in learning. In conclusion, this study recommends cartoon-like agent as the best suitable design for 2D virtual agent followed by semi-realistic, realistic and stylized designs subsequently. Finally, the findings of this study can be a useful guideline for multimedia designers in determining the ideal 2D virtual agent appearance to elicit maximum impact of it in promoting positive emotions in multimedia learning environment.

References

1. Baylor, A. L. (2011). The design of motivational agents and avatars. Educational Technology Research and Development, 59(2), 291-300.

2. Hayes-Roth, B., & Doyle, P. (1998). Animate characters. Autonomous Agents and Multi-Agent Systems, 1(2), 195–230.

3. Laurel, B. (1997). Interface agents: metaphors with character. In Software agents, Jeffrey M. Bradshaw (Ed. pp. 67-77). Cambridge, USA: MIT Press.

4. Cassell, J. (2000). Embodied conversational interface agents. Communications of the ACM, 43(4), 70–78.

5. Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review 6(1), 27-54.

6. Saidatul Maizura, S., Farah, M. Z., Nabila, A. N. K., Noorizdayantie, Samar, Zuraidah, A. R., Omar, M., Hanafi, A., & Fook, F. S. (2010). The pedagogical agent in online learning?: Effects of the degree of realism on achievement in terms of gender. Contemporary Educational Technology, 1(2), 175–185.

7. Tien, L. T., & Kamisah, O. (2010). Pedagogical agents in interactive multimedia modules: Issues of variability. Procedia - Social and Behavioral Sciences, 7(2), 605–612.

8. Schroeder, N. L., & Gotch, C. M. (2015). Persisting issues in pedagogical agent research. Journal of Educational Computing Research, 53(2), 183–204.

9. Mohammadhasani, N., Fardanesh, H., Hatami, J., Mozayani, N., & Fabio, R. A. (2018). The pedagogical agent enhances mathematics learning in ADHD students. Education and Information Technologies, 23(6), 2299–2308.

10. Vicneas, M., & Ahmad Zamzuri, M. A. (2019). The Effect of Valence and Arousal on 2D Female Virtual Agent's Designs in Multimedia Quiz App. Manuscript submitted for publication.

11. Chou, C. Y., Chan, T. W., & Lin, C. J. (2003). Redefining the learning companion: the past, present, and future of educational agents. Computers & Education, 40(3), 255-269.

12. Mohanty, A. (2016). Affective Pedagogical Agent in E-Learning Environment: A Reflective Analysis. Creative Education, 7(4), 586–595.

13. Kim, Y., & Wei, Q. (2011). The impact of learner attributes and learner choice in an agent-based environment. Computers & Education, 56(2), 505-514.

14. Payr, S. (2003). The virtual university’s faculty: an overview of educational agents. Applied Artificial Intelligence, 17(1), 1–19.

15. Baylor, A. L., & Kim, Y. (2005). Simulating Instructional Roles through Pedagogical Agents. International Journal of Artificial Intelligence in Education, 15, 95–115.

16. Kim, Y., Baylor, A. L., & Shen, E. (2007). Pedagogical agents as learning companions: The impact of agent emotion and gender. Journal of Computer Assisted Learning, 23(3), 220–234.

17. Kim, C. M., & Baylor, A. L. (2008). A virtual change agent: Motivating pre-service teachers to integrate technology in their future classrooms. Educational Technology and Society, 11(2), 309–321.

18. Ryu, J., & Baylor, A. L. (2005). The Psychometric Structure of Pedagogical Agent Persona. Technology Instruction Cognition and Learning, 2(4), 291–314.

19. Lester, J. C., Converse, S. A., Kahler, S. E., Barlow, S. T., Stone, B. A., & Bhogal, R. S. (1997). The Persona Effect: Affective Impact of Animated Pedagogical Agents. In Proceedings of the ACM SIGCHI Conference on Human factors in computing systems (pp. 359-366).

20. Graesser, A., & McNamara, D. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45(4), 234–244.

21. De Angeli, A., & Brahnam, S. (2008). I hate you! Disinhibition with virtual partners. Interacting with computers, 20(3), 302-310.

22. Fussell, S. R., Kiesler, S., Setlock, L. D., & Yew, V. (2008). How people anthropomorphize robots. In Proceedings of the 3rd international conference on Human robot interaction - HRI ’08 (p. 145-152). New York, New York, USA: ACM Press.

23. Mori, M. (2012). The uncanny valley. In K. F. MacDorman, & N. Kageki (Trans.), IEEE Robotics & Automation Magazine, 19(2), 98–100 (Original work published in 1970).

24. Winkielman, P., Berridge, K. C., & Wilbarger, J. L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 31(1), 121–135.

25. Lang, P., Bradley, M., & Cuthbert, B. (1997). Motivated attention: affect, activation, and action. Attention and Orienting: Sensory and Motivational Processes, 97–135.

26. Barrett, L. F. (1996). Hedonic tone, perceived arousal, and item desirability:three components of affective experience. Cogn. Emot. 10, 47–68.

27. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172.

28. Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17(3), 715–734.

29. Perkins, D., Wilson, G. V., & Kerr, J. H. (2001). The effects of elevated arousal and mood on maximal strength performance in athletes. Journal of Applied Sport Psychology, 13(3), 239-259.

30. Vogel, N., Ram, N., Conroy, D. E., Pincus, A. L., & Gerstorf, D. (2017). How the social ecology and social situation shape individuals’ affect valence and arousal. Emotion, 17(3), 509–527.

31. Lang, P. J., & Davis, M. (2006). Emotion, motivation, and the brain: reflex foundations in animal and human research. Progress in brain research, 156, 3-29.

32. Chajut, E., & Algom, D. (2003). Selective attention improves under stress: Implications for theories of social cognition. Journal of Personality and Social Psychology, 85(2), 231.

33. Barrett, L. F., Quigley, K., Bliss-Moreau, E., & Aronson, K. R. (2004). Arousal focus and interoceptive sensitivity. Journal of Personality and Social Psychology, 87, 684–697.

34. Kensinger, E. A. (2004). Remembering emotional experiences: The contribution of valence and arousal. Reviews in the Neurosciences, 15(4), 241-252.

35. Vesker, M., Bahn, D., Degé, F., Kauschke, C., & Schwarzer, G. (2018). Perceiving arousal and valence in facial expressions: Differences between children and adults. European Journal of Developmental Psychology, 15(4), 411–425.

36. Clark, D.R. (2017). Arousal, learning, and performance. http://nwlink.com/~donclark/leader/leadcon.html

37. Scholae, O. (2013). Emotional aspects of learning and teaching: reviewing the field − discussing the issues. Orbis Scholae, 7(2), 7-22.

38. Roxas, J. C., Richards, D., Bilgin, A., & Hanna, N. (2018). Exploring the influence of a human-like dancing virtual character on the evocation of human emotion. Behaviour and Information Technology, 37(1), 1–15.

39. Chen, C. M., & Sun, Y. C. (2012). Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners. Computers and Education, 59(4), 1273–1285.

40. Ahmad Zamzuri, M. A., & Mohd Najib, H. (2016). The effects of talking-head with various realism levels on students’ emotions in learning. Journal of Educational Computing Research, 1-15.

41. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48.

42. Chung, S., Cheon, J., & Lee, K. W. (2015). Emotion and multimedia learning: an investigation of the effects of valence and arousal on different modalities in an instructional animation. Instructional Science, 43(5), 545–559.

43. Cleveland-Innes, M., & Campbell, P. (2012). Emotional presence, learning, and the online learning environment. International Review of Research in Open and Distance Learning, 13(4), 269–292.

44. Ganotice, F. A., Datu, J. A. D., & King, R. B. (2016). Which emotional profiles exhibit the best learning outcomes? A person-centered analysis of students’ academic emotions. School Psychology International, 37(5), 498–518.

45. Hascher, T. (2010). Learning and emotion: Perspectives for theory and research. European Educational Research Journal, 9(1), 13–28.

46. Sloan, R. (2015). Virtual character design for games and interactive media. New York: A K Peters/CRC Press.

47. Gulz, A., & Haake, M. (2006). Virtual pedagogical agents–design guidelines regarding visual appearance and pedagogical roles. Current Developments in Technology-Assisted Education,© FORMATEX 2006.

48. Mayer, R. E. (2008). Applying the Science of Learning: Evidence-Based Principles for the Design of Multimedia Instruction. American Psychologist, 63(8), 760–769.

49. Gagne, R. M., Wager, W.W., Golas, K. C. & Keller, J. M (2005). Principles of Instructional Design (5th edition). California: Wadsworth

50. Bradley, M. M., & Lang, P. J. (2007). Affective Norms for English Text (ANET): Affective ratings of text and instruction manual. Technical Report. D-1, University of Florida, Gainesville, FL.

51. Yang, L. (2018, June). Developing a 24 items’ short-form of learning-related achievement emotions questionnaire (SF-L-AEQ) in Chinese students. Poster presented in the 9th European Conference on Positive Psychology, Budapest, Hungary.

52. Pekrun, R., Goetz, T., & Perry, R. P. (2005). Academic Emotions Questionnaire (AEQ). User's manual. Department of Psychology, University of Munich

53. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum

54. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). New York, NY: The Guilford Press.

55. Schneider, E., Wang, Y., & Yang, S. (2007). Exploring the Uncanny Valley with Japanese video game characters. In DiGRA Conference.

56. Wang, S., & Rochat, P. (2017). Human perception of animacy in light of the uncanny valley phenomenon. Perception, 46(12), 1386-1411.

57. Mitchell, W. J., Szerszen, K. A., Lu, A. S., Schermerhorn, P. W., Scheutz, M., & MacDorman, K. F. (2011). A mismatch in the human realism of face and voice produces an uncanny valley. I-Perception, 2(1), 10–12.

58. James, T. W., Potter, R. F., Lee, S., Kim, S., Stevenson, R. A., & Lang, A. (2015). How realistic should avatars be? An initial fMRIinvestigation of activation of the face perception network by real and animated faces. Journal of Media Psychology: Theories, Methods, and Applications, 27(3), 109-117.

59. Zell, E., Aliaga, C., Jarabo, A., Zibrek, K., Gutierrez, D., McDonnell, R., & Botsch, M. (2015). To stylize or not to stylize? The effect of shape and material stylization on the perception of computer-generated faces. ACM Transactions on Graphics, 34(6), 12.

60. Adamo-Villani, N., Lestina, J., & Anasingaraju, S. (2016). Does character’s visual style affect viewer’s perception of signing avatars? In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 160, pp. 1–8). Springer Verlag.

61. Lu, Y., Jaquess, K. J., Hatfield, B. D., Zhou, C., & Li, H. (2017). Valence and arousal of emotional stimuli impact cognitive-motor performance in an oddball task. Biological Psychology, 125, 105–114.

62. Jin, W., Gromala, D., & Tong, X. (2016). Serious game for serious disease: Diminishing stigma of depression via game experience. 2015 IEEE Games Entertainment Media Conference, GEM 2015, (October).

63. Habib, K., & Soliman, T. (2015). Cartoons’ effect in changing children mental response and behavior. Open Journal of Social Sciences, 03(09), 248–264. https://doi.org/10.4236/jss.2015.39033

64. Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses: Support for perceptual mismatch as one road to the valley of eeriness. Frontiers in Psychology, 6(MAR), 1–16.

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