Exploring Factors Affecting Acceptance Attitudes of Robot-Based Education in Special Education: Based on the Technology Acceptance Model

특수교육에서 로봇활용교육의 수용태도에 영향을 주는 요인 탐색: 기술수용모형을 바탕으로

  • 백제은 (익산궁동초등학교) ;
  • 김경현 (원광대학교 사범대학 교육학과)
  • Received : 2016.12.30
  • Accepted : 2017.02.17
  • Published : 2017.03.31

Abstract

Factors influencing the attitude towards the use of robot-based instruction in special education are explored using the technology acceptance model (TAM). Their interrelatedness is also analyzed. Research data were obtained via a questionnaire survey of elementary, middle, and high school special education teachers in North Chungcheong Province. The results reveal that three factors influence the attitude towards using robot-based instruction in special education: perceived usefulness, perceived ease of use, and social influence. Of these, perceived usefulness exerts the strongest influence. Perceived ease of use was found to be influenced by personal innovation and social influence, and perceived usefulness is influenced by perceived ease of use and personal innovation. Efforts should be made to induce a receptive attitude towards the use of robot-based instruction among teachers for its stable acceptance.

기술수용모형(TAM)을 바탕으로 특수교육에서 로봇활용교육의 수용태도에 영향을 미치는 요인을 탐색하여 이들 간의 관계를 검증하였다. 이를 위해 충청북도 초 중 고등학교 특수교사들을 대상으로 설문 조사를 실시하였다. 연구 결과, 특수교육에서 로봇활용교육의 수용태도에 영향을 미치는 요인은 인지된 유용성, 인지된 용이성, 사회적 영향력의 3개 요인이며, 이중 가장 크게 영향을 미치는 것은 인지된 유용성으로 나타났다. 또한 인지된 용이성에 영향을 미치는 요인은 혁신성향과 사회적 영향력으로 나타났다. 인지된 유용성에 영향을 미치는 요인은 인지된 용이성과 혁신성향으로 밝혀졌다. 로봇활용교육이 특수교육에 안정적으로 수용되기 위해서는 로봇활용교육에 대한 교사의 긍정적 인식을 이끌어 내는 노력이 필요하다.

Keywords

References

  1. Pioggia, G., Igliozzi, R., Ferro, M., Ahluwalia, A., Muratori, F., & De Rossi, D. (2005). An android for enhancing social skills and emotion recognition in people with autism. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 13(4), 507-515. https://doi.org/10.1109/TNSRE.2005.856076
  2. Graham-Rowe, D. (2002). My best friend's a robot. New scientist(2369), 30-33.
  3. Dautenhahn, K. (2003). Roles and functions of robots in human society: implications from research in autism therapy. Robotica, 21(04), 443-452. https://doi.org/10.1017/S0263574703004922
  4. Robins, B., Dautenhahn, K., & Dubowski, J. (2006). Does appearance matter in the interaction of children with autism with a humanoid robot?. Interaction Studies, 7(3), 509-542. https://doi.org/10.1075/is.7.3.16rob
  5. Cook, A. M., Adams, K., Encarnacao, P., & Alvarez, L. (2012). The role of assisted manipulation in cognitive development. Developmental neurorehabilitation, 15(2), 136-148. https://doi.org/10.3109/17518423.2011.635609
  6. Kimbler, D. (1984). Robots and special education: The robot as extension of self 1. Peabody Journal of Education, 62(1), 67-76. https://doi.org/10.1080/01619568409538465
  7. Howell, R., & Hay, K. (1989). Software-based access and control of robotic manipulators for severely physically disabled students. Journal of Artificial Intelligence in Education, 1(1), 53-72.
  8. Robins, B., Dautenhahn, K., Te Boekhorst, R., & Billard, A. (2005). Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills?. Universal Access in the Information Society, 4(2), 105-120. https://doi.org/10.1007/s10209-005-0116-3
  9. Gelderblom, G. J., Dijkstra, J., & Kronreif, G. (2007). User involvement in IROMEC: Robot development for children with disabilities. Proc. AAATE07, 515-519.
  10. Martens, C., Ruchel, N., Lang, O., Ivlev, O., & Graser, A. (2001). A friend for assisting handicapped people. Robotics & Automation Magazine, IEEE, 8(1), 57-65. https://doi.org/10.1109/100.924364
  11. Michaud, F., Larouche, H., Larose, F., Salter, T., Duquette, A., Mercier, H., & Lauria, M. (2007). Mobile robots engaging children in learning. Paper presented at the Canadian Medical and Biological Engineering Conference, Toronto.
  12. Mohan, R. E., Calderon, C. A. A., Zhou, C., & Yue, P. K. (2008). Evaluating virtual emotional expression systems for human robot interaction in rehabilitation domain. Paper presented at the Cyberworlds, 2008 International Conference on.
  13. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  14. 박광렬(2011). 초등학교 로봇 교육 및 교구의 현황과 발전 방향의 고찰. 한국실과교육학회지, 24(3), 323-343.
  15. Alimisis, D. (2009). Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods. School of Pedagogical and Technological Education, Αthens.
  16. 김태준(2017). 로봇활용교육을 위한 특수교사의 수용의도에 관한 구조 분석: 기술수용모형을 바탕으로. 미출판 박사학위논문, 전남대학교 대학원 특수교육과.
  17. Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
  18. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  19. Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision sciences, 28(2), 357-389. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
  20. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6
  21. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605. https://doi.org/10.1016/0305-0483(95)00035-6
  22. McFarland, D., & Hamilton, D. (2005). Factors affecting student performance and satisfaction: Online versus traditional course delivery. The Journal of Computer Information Systems, 46(2), 25.
  23. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.
  24. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
  25. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
  26. Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision sciences, 28(3), 557-582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
  27. Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268. https://doi.org/10.1016/j.jsis.2005.07.003
  28. Rogers Everett, M. (1995). Diffusion of innovations. New York.
  29. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test*. Decision sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x
  30. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222. https://doi.org/10.1287/isre.2.3.192
  31. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6): Pearson Prentice Hall Upper Saddle River, NJ.
  32. Bajaj, A., & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information & management, 33(4), 213-224. https://doi.org/10.1016/S0378-7206(98)00026-3