Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

A study on the emotional changes of learners according to the emotions provided by virtual characters

가상 캐릭터가 제공하는 감정에 따른 학습자의 감정적 반응에 관한 연구

  • 최동연 (건양사이버대학교 교양학부)
  • Received : 2022.03.08
  • Accepted : 2022.05.20
  • Published : 2022.05.28
Download PDF
⟨ Previous Next ⟩

Abstract

Considerable interest has been directed toward utilizing virtual environment-based simulations for teacher education which provide authentic experience of classroom environment and repetitive training. Emotional Interaction should be considered for more advanced simulation learning performance. Since emotion is important factors in creative thinking, inspiration, concentration, and learning motivation, identifying learners' emotional interactions and applying these results to teaching simulation is essential activities. In this context, this study aims to identify the objective data for the empathetic response through the movement of the learner's EEG (Electroencephalogram) and eye-tracking, and to provide clues for designing emotional teaching simulation. The results of this study indicated that intended empathetic response was provided and in terms of valence (positive and negative) states and situational interest played an important role in determining areas of interest. The results of this study are expected to provide guidelines for the design of emotional interactions in simulations for teacher education as follow; (a) the development of avatars capable of expressing sophisticated emotions and (b) the development of scenarios suitable for situations that cause emotional reactions.

실제 교실 환경과 반복 훈련을 제공하는 가상 환경 기반 시뮬레이션을 교사교육에 활용하는 데 상당한 관심이 집중되고 있다. 시뮬레이션 학습환경이 더욱 정교하게 학습에 적용되기 위해서는 감정적 상호작용을 고려할 필요가 있다. 감성은 창의적 사고, 영감, 집중력, 학습동기에 중요한 요소이기 학습자와의 정서적 상호작용을 파악하고 이를 교수 시뮬레이션에 적용하는 것은 필요한 활동이다. 본 연구는 학습자의 EEG(Electroencephalogram)와 시선추적을 통해 공감적 반응을 위한 객관적인 데이터를 확인하고, 감성교수 시뮬레이션 설계를 위한 단서를 제공하는 것을 목적으로 한다. 연구의 결과는 의도된 공감적 반응이 제공되었고, 상황적 감성이 학습자의 정서 반응을 결정하는 데 중요한 역할을 하고 있음을 확인하였다. 본 연구의 결과는 정교한 감정을 표현할 수 있는 아바타의 개발 및 감정적 반응을 일으키는 상황에 적합한 시나리오의 개발이라는 측면에서 교사교육 시뮬레이션의 설계에 시사점을 갖는다.

Keywords

References

  1. F. A. Drews & J. Z. Bakdash. (2013). Simulation training in health care. Reviews of Human Factors and Ergonomics, 8(1), 191-234. DOI : 10.1177/1557234X13492977
  2. J. Lu, P. Hallinger & P. Showanasai. (2014). Simulation-based learning in management education: A longitudinal quasi-experimental evaluation of instructional effectiveness. Journal of Management Development, 33(3), 218-244. DOI : 10.1108/jmd-11-2011-0115
  3. S. M. Boyne, (2012). Crisis in the classroom: using simulations to enhance decision-making skills. J. Legal Educ, 62, 311. DOI : 10.2139/ssrn.2103603
  4. E. G. Bradley & B. Kendall. (2014). A review of computer simulations in teacher education. Journal of Educational Technology Systems, 43(1), 3-12. DOI : 10.2190/ET.43.1.b
  5. A. T. Hayes, C. L. Straub, L. A. Dieker, C. E. Hughes & M. C. Hynes. (2013). Ludic learning: Exploration of TLE TeachLivETM and effective teacher training. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 5(2), 20-33. DOI : 10.4018/jgcms.2013040102
  6. J. P. Guilford & R. Hoepfner. (1971). The analysis of intelligence. McGraw-Hill Companies. DOI : 10.7771/2380-176x.1876
  7. D. Brozik & A. Zapalska. (2002). The PORTFOLIO GAME: Decision making in a dynamic environment. Simulation & Gaming, 33(2), 242-255. DOI : 10.1177/1046878102033002013
  8. G. H. Tompson & P. Dass. (2000). Improving students' self-efficacy in strategic management: The relative impact of cases and simulations. Simulation & Gaming, 31(1), 22-41. DOI : 10.1177/104687810003100102
  9. M. El Ayadi, M. S. Kamel & F. Karray. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern recognition, 44(3), 572-587. DOI : 10.1016/j.patcog.2010.09.020
  10. Z. Lan, O. Sourina, L. Wang & Y. Liu. (2014, October). Stability of features in real-time EEG-based emotion recognition algorithm. In 2014 International Conference on Cyber worlds (pp. 137-144). IEEE. DOI : 10.1109/cw.2014.27
  11. J. A. Coan & J. J. Allen. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological psychology, 67(1), 7-50. DOI : 10.1016/j.biopsycho.2004.03.002
  12. B. Jaskula, K. Pancerz & J. Szkola. (2015). Toward Synchronization of EEG and Eye-tracking Data Using an Expert System. InCS&P(pp. 196-198). DOI : 10.5220/0005094600790086
  13. J. L. Plass, S. Heidig, E. O. Hayward, B. D. Homer & E. Um. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128-140. DOI : 10.1016/j.learninstruc.2013.02.006
  14. R. E. Mayer. (2005). Cognitive theory of multimedia learning. The Cambridge handbook of multimedia learning. DOI : 10.1017/cbo9781139164603.004
  15. S. D'Mello, B. Lehman, R. Pekrun & A. Graesser. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153-170. DOI : 10.1016/j.learninstruc.2012.05.003
  16. U. I. Magner, R. Schwonke, V. Aleven, O. Popescu & A. Renkl. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning and Instruction, 29, 141-152. DOI : 10.1016/j.learninstruc.2012.07.002
  17. P. Ekman. (1993). Facial expression and emotion. American psychologist, 48(4), 384. DOI : 10.1017/cbo9780511752841.027
  18. M. A. Nicolaou, H. Gunes & M. Pantic. (2011). Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space. IEEE Transactions on Affective Computing, 2(2), 92-105. DOI : 10.1109/t-affc.2011.9