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An Analysis Prospective Mathematics Teachers' Perception on the Use of Artificial Intelligence(AI) in Mathematics Education

수학교육에서 인공지능(AI) 활용에 관한 예비수학교사의 인식 분석

  • Received : 2020.07.27
  • Accepted : 2020.08.26
  • Published : 2020.09.30

Abstract

With the advent of the AI, the need to use AI in the field of education is widely recognized. The purpose of this study is to shed light on how prospective mathematics teachers perceive the need for AI and the role of teachers in future mathematics education. As a result, with regard to teaching, prospective teachers recognized that the use of AI in school mathematics is a demand of a new era, that various types of lesson can be implemented, and that accurate knowledge and information can be delivered. On the other hand, they recognized that AI has limitations in having cognitive and emotional interactions with students. As for mathematics learning, the prospective teachers recognized that AI can provide individualized learning, be used for supplementary learning outside of school, and stimulate students' interest in learning. However, they also said that learning through AI could undermine students' ability to think on their own. With regard to assessment, the prospective teachers recognized that AI is objective, fair and can reduce teachers' workload, but they also said that AI has limitations in evaluating students' abilities in constructed-response items and in process-focused assessment. The roles of teachers that the prospective teachers think were to conduct a lesson, emotional interaction, unstructured assessment, and counseling, and those of AI were individualized learning, rote learning, structured assessment, and administrative works.

AI 시대의 함께 교육에서도 AI 활용의 필요성이 제기된다. 본 연구의 목적은 예비수학교사가 인식하는 미래 수학교육에서 AI의 필요성과 AI 활용에서 교사의 역할을 조명하는 것이다. 연구 결과, 교수 측면에서 예비교사들은 학교 수학에 AI 활용이 시대적 요구이며, 다양한 유형의 수업 구현과 정확한 지식 및 정보를 전달할 수 있지만, 인지적·감정적 상호작용에 한계가 있다고 하였다. 학습 측면에서 AI는 개별화 학습을 제공하고, 학교 수업 외 보충학습에 활용할 수 있고, 학습 흥미를 자극할 수 있지만, 학생들의 주체적 사고 능력을 저해할 수 있다고 하였다. 평가의 측면에서 AI는 객관적이고 공정하며 교사의 업무를 감소할 수 있지만 서·논술형 문항과 과정 중심 평가에서 한계가 있다고 하였다. AI 활용에서 예비교사들이 생각하는 교사의 역할은 수업, 감정적 상호작용, 비정형화된 평가, 상담이었고, AI의 역할은 개별화 학습, 기계적 학습, 정형화된 평가와 행정 업무로 나타났다.

Keywords

References

  1. Ministry of Science & ICT (2019). National strategy for artificial intelligence. Retrieved from https://www.msit.go.kr/web/msipContents/contentsView.do?cateId=_policycom2&artId=2405727
  2. Kyun, S., Yi, J., & Kwon, S. (2018). Students' perception of universities' introduction of artificial intelligence and of the artificial intelligence professors. The Journal of Educational Research, 16(3), 77-101.
  3. Kim, D.-J., Shin, J., Lee, J., Lim, W., Lee, Y., & Choi, S. (2019). Conceptualizing discursive teaching capacity: A case study of a middle school mathematics teacher, School Mathematics, 21(2), 291-318. https://doi.org/10.29275/sm.2019.06.21.2.291
  4. Kim, H-K., Park, C., Jeong, S., & Ko, H. K. (2018). A view on complementary relation of human teacher and AI teacher in future education, Journal of Education & Culture, 24(6), 189-207.
  5. Nam M. (2018). The characteristics of teacher perceptions on the school education changes in intelligent information society. The Journal of Educational Development, 38(2), 129-153.
  6. Park, S., & Ihm, H-J. (2019). Elementary English teachers' perception toward future of English education. The Journal of Education, 39(4), 123-144.
  7. Park, J. H., & Shin, N. M. (2017). Students' perceptions of artificial intelligence technology and artificial intelligence teachers. The Journal of Korean Teacher Education, 34(2), 169-192. https://doi.org/10.24211/TJKTE.2017.34.2.169
  8. Suk, J-Y., & Yi, S-W. (2018). The role of teachers in the age of artificial intelligence- A case of the tasks for Korean teachers. Humanities Research, 55, 361-390.
  9. Song, S. C., & Shim, K. C. (2017). A study on the awareness of pre-service science teachers about secondary education in future intelligence information society. Biology Education, 45(3), 404-417. https://doi.org/10.15717/bioedu.2017.45.3.404
  10. Ryu, K., Jung, J. W., Kim, Y. S., & Kim, H. B. (2018). Understanding qualitative research methods. Seoul; Park Young Sa.
  11. Lim, J. H., Ryu, K. H., & Kim, B. C. (2017). An exploratory study on the direction of education and teacher competencies in the 4th industrial revolution. The Journal of Korean Education, 44(2), 5-32.
  12. Choi, M-Y. & Lee, T-W. (2019). The status of Artificial Intelligence in education and Prediction of change in roles of teacher and school. Conference Paper of the Korean Association of Computer Education, 23(2), 85-88.
  13. Aksoy, E., Narli, S., & Idil, F. H. (2016). Using data mining techniques examination of the middle school students' attitude towards mathematics in the context of some variables. International Journal of Education in Mathematics Science and Technology, 4(3), 210-228. https://doi.org/10.18404/ijemst.02496
  14. Ally, M. (2019). Competency profile of the digital and online teacher in future education. International Review of Research in Open and Distributed Learning, 20(2), 302-318. https://doi.org/10.19173/irrodl.v20i2.4206
  15. Araya, R., Jimenez, A., Bahamondez, M., Calfucura, P., Dartnell, P., & Soto-Andrade, J. (2014). Teaching modeling skills using a massively multiplayer online mathematics game. World Wide Web, 17(2), 213-227. https://doi.org/10.1007/s11280-012-0173-5
  16. Bywater, J. P., Chiu, J. L., Hong, J., & Sankaranarayanan, V. (2019). The teacher responding tool: Scaffolding the teacher practice of responding to student ideas in mathematics classrooms. Computers & Education, 139, 16-30. https://doi.org/10.1016/j.compedu.2019.05.004
  17. Cabestrero, R., Quiros, P., Santos, O. C., Salmeron-Majadas, S., Uria-Rivas, R., Boticario, J. G., ... & Ferri, F. J. (2018). Some insights into the impact of affective information when delivering feedback to students. Behaviour & Information Technology, 37(12), 1252-1263. https://doi.org/10.1080/0144929X.2018.1499803
  18. Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24. https://doi.org/10.1016/j.procs.2018.08.233
  19. Cope, B., Kalantzis, M., & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory. Advance online publication.
  20. Cukurova, M., Kent, C., & Luckin, R. (2019). Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring. British Journal of Educational Technology, 50(6), 3032-3046. https://doi.org/10.1111/bjet.12829
  21. Dutton, T., Barron, B., & Boskovic, G. (2018). Building an ai world: Report on national and regional ai strategies. Retrieved from https://www.cifar.ca/docs/default-source/ai-society/buildinganaiworld_eng.pdf
  22. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
  23. Gabriel, F., Signolet, J., & Westwell, M. (2018). A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy. International Journal of Research & Method in Education, 41(3), 306-327. https://doi.org/10.1080/1743727X.2017.1301916
  24. Guilherme, A. (2019). AI and education: the importance of teacher and student relations. AI & Society, 34(1), 47-54. https://doi.org/10.1007/s00146-017-0693-8
  25. Holmes, W., Bialik, M., & Fadel, C. (2019). 인공지능 시대의 미래교육 (정제영, 이선복 역). 서울: 박영스토리.
  26. Huang, X., Craig, S. D., Xie, J., GraManyikeesser, A., & Hu, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258-265. https://doi.org/10.1016/j.lindif.2016.01.012
  27. Ibanez, M. B., Di-Serio, A., Villaran-Molina, D., & Delgado-Kloos, C. (2015). Support for augmented reality simulation systems: The effects of scaffolding on learning outcomes and behavior patterns. IEEE Transactions on Learning Technologies, 9(1), 46-56. https://doi.org/10.1109/TLT.2015.2445761
  28. Jacobs, V. R., Lamb, L. L., & Philipp, R. A. (2010). Professional noticing of children's mathematical thinking. Journal for Research in Mathematics Education, 41(2), 169-202. https://doi.org/10.5951/jresematheduc.41.2.0169
  29. Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers & Education, 127, 233-251. https://doi.org/10.1016/j.compedu.2018.08.023
  30. Li, W., Chiu, C. K., & Tseng, J. C. (2019). Effects of a personalized navigation support approach on students' context-aware ubiquitous learning performances. Journal of Educational Technology & Society, 22(2), 56-70.
  31. Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. New York: McKinsey Global Institute.
  32. Martin, T., Smith, C. P., Forsgren, N., Aghababyan, A., Janisiewicz, P., & Baker, S. (2015). Learning fractions by splitting: Using learning analytics to illuminate the development of mathematical understanding. Journal of the Learning Sciences, 24(4), 593-637. https://doi.org/10.1080/10508406.2015.1078244
  33. Masci, C., Johnes, G., & Agasisti, T. (2018). Student and school performance across countries: A machine learning approach. European Journal of Operational Research, 269(3), 1072-1085. https://doi.org/10.1016/j.ejor.2018.02.031
  34. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12-14.
  35. Pai, K. C., Kuo, B. C., Liao, C. H., & Liu, Y. M. (2020). An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning. Educational Psychology. Advance online publication.
  36. Phillips, A., Pane, J. F., Reumann-Moore, R., & Shenbanjo, O. (2020). Implementing an adaptive intelligent tutoring system as an instructional supplement. Educational Technology Research and Development. Advance online publication.
  37. Rajendran, R., Iyer, S., & Murthy, S. (2018). Personalized affective feedback to address students' frustration in ITS. IEEE Transactions on Learning Technologies, 12(1), 87-97. https://doi.org/10.1109/TLT.2018.2807447
  38. Reinhold, F., Hoch, S., Werner, B., Richter-Gebert, J., & Reiss, K. (2020). Learning fractions with and without educational technology: What matters for high-achieving and low-achieving students?. Learning and Instruction. Advance online publication.
  39. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599. https://doi.org/10.1007/s40593-016-0110-3
  40. Russell, S, J., & Norvig, P. (2016). Artificial intelligence: A modern approach. New Jersey: Prentice Hall.
  41. Ryoo, K., & Linn, M. C. (2016). Designing automated guidance for concept diagrams in inquiry instruction. Journal of Research in Science Teaching, 53(7), 1003-1035. https://doi.org/10.1002/tea.21321
  42. Schwarz, B. B., Prusak, N., Swidan, O., Livny, A., Gal, K., & Segal, A. (2018). Orchestrating the emergence of conceptual learning: A case study in a geometry class. International Journal of Computer-Supported Collaborative Learning, 13(2), 189-211. https://doi.org/10.1007/s11412-018-9276-z
  43. Shin, D., & Shim, J. (2020). A systematic review on data mining for mathematics and science education. International Journal of Science And Mathematics Education. Advance online publication.
  44. VanLehn, K., Burkhardt, H., Cheema, S., Kang, S., Pead, D., Schoenfeld, A., & Wetzel, J. (2019). Can an orchestration system increase collaborative, productive struggle in teaching-by-eliciting classrooms?. Interactive Learning Environments. Advance online publication.
  45. Hu, W., & Shi, Y. B. (2018). Research on the role predicament of teachers in the era of artificial intelligence. US-China Education Review, 8(6), 273-278.
  46. Wu, H. M., Kuo, B. C., & Wang, S. C. (2017). Computerized dynamic adaptive tests with immediately individualized feedback for primary school mathematics learning. Journal of Educational Technology & Society, 20(1), 61-72.
  47. Zhao, Y., & Liu, G. (2019, January). How do teachers face educational changes in artificial intelligence era. 2018 International Workshop on Education Reform and Social Sciences (pp. 47-50). Chengdu, China.
  48. Zhou, J-S., & Dai, J-C. (2019, June). The transformation of teachers' role in the new era of intelligence education. 2019 International Conference on Advanced Education and Management (pp. 297-301). Chengdu, China.

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