DOI QR코드

DOI QR Code

수학 수업에서 예비교사의 인공지능 프로그램 '똑똑! 수학 탐험대' 사용 의도 이해: 자기효능감과 인공지능 불안, 기술수용모델을 중심으로

Preservice teacher's understanding of the intention to use the artificial intelligence program 'Knock-Knock! Mathematics Expedition' in mathematics lesson: Focusing on self-efficacy, artificial intelligence anxiety, and technology acceptance model

  • 투고 : 2023.07.19
  • 심사 : 2023.08.13
  • 발행 : 2023.08.31

초록

본 연구는 기술수용모델을 기반으로 예비교사의 자기효능감과 AI 불안이 수학 수업에서 '똑똑! 수학 탐험대'를 사용하려는 의도에 미치는 영향을 구조적으로 살펴보았다. 이를 위해 254명의 예비교사들의 자기효능감, AI 불안, 인지된 사용 용이성, 인지된 유용성, 사용 의도를 변인으로 연구모형을 설정하고 구조방정식으로 변인 간의 구조적 관계와 직·간접효과를 분석하였다. 분석 결과, 자기효능감은 인지된 사용 용이성, 인지된 유용성, 사용 의도에 유의미한 영향을 미쳤으며, AI 불안은 인지된 사용 용이성과 인지된 유용성에 유의미한 영향을 미치지 않았다. 인지된 사용 용이성은 인지된 유용성과 사용 의도에 유의미한 영향을 미쳤으며, 인지된 유용성은 사용 의도에 유의미한 영향을 미쳤다. 이러한 결과를 통해 수학수업에서 예비교사가 '똑똑! 수학 탐험대' 사용을 촉진하기 위한 시사점과 방안을 제안하였다.

This study systematically examined the influence of preservice teachers' self-efficacy and AI anxiety, on the intention to use AI programs 'knock-knock! mathematics expedition' in mathematics lessons based on a technology acceptance model. The research model was established with variables including self-efficacy, AI anxiety, perceived ease of use, perceived usefulness, and intention of use from 254 pre-service teachers. The structural relationships and direct and indirect effects between these variables were examined through structural equation modeling. The results indicated that self-efficacy significantly affected perceived ease of use, perceived usefulness, and intention to use. In contrast, AI anxiety did not significantly influence perceived ease of use and perceived usefulness. Perceived ease of use significantly affected perceived usefulness and intention to use and perceived usefulness significantly affected intention to use. The findings offer insights and strategies for encouraging the use of 'knock-knock! mathematics expedition' by preservice teachers in mathematics lessons.

키워드

참고문헌

  1. Ajzen. I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hal.
  2. Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers' readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 1-11. https://doi.org/10.1016/j.caeai.2022.100099
  3. Bae, B. (2011). Amos 19 structural equation modeling principle and practice. Cheongram
  4. Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373. https://doi.org/10.1521/jscp.1986.4.3.359
  5. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: Personal computer adoption and use as an Illustration. Technology Studies, 2(2), 285-309.
  6. Bentler, P. M. (1989). EQS structural equations program manual. BMDP.
  7. Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Sage.
  8. Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013). Acceptance of game-based learning by secondary school teachers. Computers & Education, 67, 21-35. https://doi.org/10.1016/j.compedu.2013.02.010
  9. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
  10. Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students' behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
  11. Chang, H. & Nam, J. (2021). The use of artificial intelligence in elementary mathematics education -Focusing on the math class support system "Knock-knock! Math Expedition"-. The Journal of Korea Elementary Education, 31(Supplement), 105-123. http://doi.org/10.20972/kjee.31..202101.105
  12. Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
  13. Chapman, O. (2002). Belief structure and inservice high school mathematics teacher growth. In G. Leder, E. Pehkonen, & G. Torner (Eds.), Beliefs: A hidden variable in mathematics education? (pp. 177-193). Kluwer.
  14. Chiu, T. K. (2017). Introducing electronic textbooks as daily-use technology in schools: A top-down adoption process. British Journal of Educational Technology, 48(2), 524-537. https://doi.org/10.1111/bjet.12432
  15. Chocarro, R., Cortinas, M., & Marcos-Matas, G. (2023). Teachers' attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users' characteristics. Educational Studies, 49(2), 295-313. https://doi.org/10.1080/03055698.2020.1850426
  16. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation, MIT Sloan School of Management].
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  18. 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
  19. DeVita, M., Verschaffel, L., & Elen, J. (2012). Acceptance of interactive whiteboards by Italian mathematics teachers. Educational Research, 3(7), 553-565.
  20. Fearnley, M. R., & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education, 8(2), 89-106. https://doi.org/10.22492/ije.8.2.05
  21. Gavora, P. (2010). Slovak pre-service teacher self-efficacy: Theoretical and research considerations. The New Educational Review, 21(2), 17-30.
  22. Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374.
  23. Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26(4), 4709-4729. https://doi.org/10.1007/s10639-021-10493-4
  24. Ha, J. G., Page, T., & Thorsteinsson, G. (2011). A study on technophobia and mobile device design. International Journal of Contents, 7(2), 17-25. https://doi.org/10.5392/IJoC.2011.7.2.017
  25. Hair, J. F., Black, W., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Pearson.
  26. Henson, R. K. (2001). Teacher self efficacy: Substantive implications and measurement dilemmas. Presented at the Annual Meeting of the Educational, Texas A & M University.
  27. Hong, J. C., Hwang, M. Y., Tsai, C. M., Liu, M. C., & Lee, Y. F. (2022). Exploring teachers' attitudes toward implementing new ICT educational policies. Interactive Learning Environments, 30(10), 1823-1837. https://doi.org/10.1080/10494820.2020.1752740
  28. Hoy, A. W. (2000). Changes in teacher efficacy during the early years of teaching. Paper presented at the Annual Meeting of the American Educational Research Association Conference, New Orleans, LA.
  29. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  30. Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers' perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24, 2653-2675. https://doi.org/10.1007/s10639-019-09925-z
  31. Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270. https://doi.org/10.1002/asi.23867
  32. Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers' intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Journal of Educational Technology & Society, 21(3), 48-59.
  33. Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications.
  34. Kramarski, B., & Michalsky, T. (2010). Preparing preservice teachers for self-regulated learning in the context of technological pedagogical content knowledge. Learning and Instruction, 20(5), 434-447. https://doi.org/10.1016/j.learninstruc.2009.05.003
  35. Langran, E., Searson, M., Knezek, G., & Christensen, R. (2020, April). AI in teacher education. In Society for Information Technology & Teacher Education International Conference (pp. 751-756). Association for the Advancement of Computing in Education (AACE).
  36. Lim, M., Kim, H. M., Nam, J., & Hong, O. (2021). Exploring the application of elementary mathematics supporting system using artificial intelligence in teaching and learning. School Mathematics, 23(2), 251-270. http://doi.org/10.29275/sm.2021.06.23.2.251
  37. Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410. https://doi.org/10.1016/j.techsoc.2020.101410
  38. Lin, P., & Van Brummelen, J. (2021, May). Engaging teachers to co-design integrated AI curriculum for K-12 classrooms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-12).
  39. Ministry of Education (2020a). Comprehensive plan for science, mathematics, information, and STEAM education. Ministry of Education
  40. Ministry of Education (2020b, September 14). Artificial intelligence, into school! Artificial intelligence (AI), introduced as an elementary math study helper, as an optional course in high school. News from the Ministry of Education. https://www.moe.go.kr/boardCnts/view.do?boardID=294&board -Seq=81918&lev=0&searchType=null&statusYN=W&page=5&s=moe&m=020402&opType=N
  41. Moon, S. (2009). Basic concepts and application of structural equation modeling. Hakjisa.
  42. Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51(4), 1523-1537. https://doi.org/10.1016/j.compedu.2008.02.003
  43. National Council of Teachers of Mathematics (2000). Principles and Standards for School Mathematics. NCTM.
  44. Park, M. (2020). Applications and possibilities of artificial intelligence in mathematics education. Communications of Mathematical Education, 34(4), 545-561. https://doi.org/10.7468/jksmee.2020.34.4.545
  45. Rafique, H., Ul Islam, Z., & Shamim, A. (2023). Acceptance of e-learning technology by government school teachers: Application of extended technology acceptance model. Interactive Learning Environments, 1-19. https://doi.org/10.1080/10494820.2022.2164783
  46. Saade, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8(1), 177-191. https://doi.org/10.28945/166
  47. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
  48. Segars, A. H., & Grover, V. (1998). Strategic information systems planning success: An investigation of the construct and its measurement. MIS Quarterly, 22(2), 139-163.
  49. Seufert, S., Guggemos, J., & Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552. https://doi.org/10.1016/j.chb.2020.106552
  50. Shin, D. (2020). An analysis prospective mathematics teachers' perception on the use of artificial intelligence(AI) in mathematics education. Communications of Mathematical Education, 34(3), 215-234. https://doi.org/10.7468/jksmee.2020.34.3.215
  51. Sinclair, J., & Aho, A. (2018). Experts on super innovators: Understanding staff adoption of learning management systems. Higher Education Research and Development, 37(1), 158-172. https://doi.org/10.1080/07294360.2017.1342609
  52. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006
  53. Teo, T., Milutinovic, V., Zhou, M., & Bankovic, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), 811-827. https://doi.org/10.1080/10494820.2016.1189943
  54. Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783-805. https://doi.org/10.1016/S0742-051X(01)00036-1
  55. Van Vaerenbergh, S., Perez-Suay, A., & Diago, P. D. (2023). Acceptance and intentions of using dynamic geometry software by pre-service primary school teachers. Education Sciences, 13(7), 661. https://doi.org/10.3390/educsci13070661
  56. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
  57. 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
  58. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178.
  59. Woo, J. (2012). Structural equation model concept and understanding. Hannarae.
  60. Yim, Y., Ahn, S., Kim, K. M., Kim, J. H., & Hong, O. (2021). The effects of an AI-based class support system on student learning: Focusing on the case of toc-toc math expedition in Korea. Korean Journal of Elementary Education, 32(4), 61-73. http://doi.org/10.20972/kjee.32.4.202112.61