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AI-Based Educational Platform Analysis Supporting Personalized Mathematics Learning

개별화 맞춤형 수학 학습을 지원하는 AI 기반 플랫폼 분석

  • Received : 2022.08.25
  • Accepted : 2022.09.26
  • Published : 2022.09.30

Abstract

The purpose of this study is to suggest implications for mathematics teaching and learning when using AI-based educational platforms that support personalized mathematics learning. To this end, we selected five platforms(Knock-knock! Math Expedition, knowre, Khan Academy, MATHia, CENTURY) and analyzed how the AI-based educational platforms for mathematics reflect the three elements(PLP, PLN, PLE) to support personalized learning. The results of this study showed that although the characteristics of PLP, PLN, and PLE implemented on each platform varied, they were designed to form PLEs that allow learners to make their autonomous decisions about learning based on PLP and PLN. The significance of this study can be found in that it has improved the understanding and practicability of personalized mathematics learning with the AI-based educational platforms.

본 연구의 목적은 개별화 맞춤형 수학 학습을 지원하는 AI 기반 플랫폼 활용 시 고려해야 할 교수·학습에 관한 시사점을 제안하는 것이다. 이를 위해 국내·외 공교육에서 활용되고 있는 플랫폼 5개(똑똑!수학탐험대, 노리AI스쿨수학, 칸 아카데미, MATHia, CENTURY)를 분석대상으로 선정하여, AI 기반 수학교육 플랫폼이 개별화 맞춤형 학습을 지원하기 위한 세 가지 요소(PLP, PLN, PLE)를 어떻게 반영하고 있는지를 분석하였다. 그 결과, 각 플랫폼에서 구현하고 있는 PLP, PLN, PLE의 특징은 다양했지만, PLP와 PLN을 바탕으로 학습자가 자율적으로 학습에 대한 의사결정을 내릴 수 있는 PLE를 형성할 수 있도록 설계된 것으로 분석되었다. 본 연구의 의의는 AI 기반 수학교육 플랫폼을 활용하는 개별화 맞춤형 수학 학습에 대한 이해도와 실천 가능성을 높였다는 데에서 찾을 수 있다.

Keywords

References

  1. Joint Ministries (2020). Education Policy Direction and Core Tasks in the Age of Artificial Intelligence. Retrieved from https://www.moe.go.kr/boardCnts/viewRenew.do?boardID=294&lev=0&statusYN=W&s=moe&m=020402&opType=N &boardSeq=82674
  2. Ministry of Education (2015). Mathematics Curriculum (Ministry of Education Notice 2015-74 [Attachment 8]). Retrieved from http://ncic.re.kr/mobile.index2.do
  3. Kim, T. E., Kwon, S. K., Park, J., Lee, M., Jo, Y. D., & Lee, K. H. (2020). The process of elementary and middle school underachievers' growth in learning: a longitudinal case study (IV). KICE.
  4. Kim, H., & Jeong, W. Y. (2021). A Qualitative Study on the Distance Education Class Experiences of Early Childhood Preservice Teachers due to COVID-19. The Journal of Learner-Centered Curriculum and Instruction, 21(2), 485-508.
  5. Park, M. (2020a). The trends of using artificial intelligence in mathematics education. The Journal of Korea Elementary Education, 31(Supplement), 91-102.
  6. Park, M. (2020b). 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
  7. Part, M., Lim, H., Kim, J., Lee, K., & Kim, M. (2020). The effects on the personalized learning platform with machine learning recommendation modules: Focused on learning time, self-directed learning ability, attitudes toward mathematics, and mathematics achievement. The Mathematical Education, 59(4), 373-387. https://doi.org/10.7468/MATHEDU.2020.59.4.373
  8. Park, S. I. (2008). Retrospect and prospect of 'individualized learning'. The Korean Journal of Educational Methodology Studies, 20(1), 1-22. https://doi.org/10.17927/TKJEMS.2008.20.1.1
  9. Park, H. Y., Son, B. E., & Ko, H. K. (2022). Study on the mathematics teaching and learning artificail intelligence platform analysis. Communications of Mathematical Education, 36(1), 1-21. https://doi.org/10.7468/JKSMEE.2022.36.1.1
  10. Bang, M. (2018). The Educational Implementation of e-Learning System Applying the Theory of Motivation - With Focus on "Interest" and the "Self-Determination Theory". Journal of digital convergence, 16(11), 69-79. https://doi.org/10.14400/JDC.2018.16.11.069
  11. Bae, Y. J. (2010). A qualitative study on self-directed Learning in cyber space. The Journal of Curriculum Studies, 28(2), 205-223. https://doi.org/10.15708/kscs.28.2.201006.008
  12. Busan Metropolitan City Office of Education (2019). Artificial Intelligence Based Education for Future. Uhga.
  13. Seoul Metropolitan Office of Education (2022.03.30.). The Seoul Metropolitan Office of Education signed an agreement on Alef Education business. Retrieved from https://enews.sen.go.kr/news/view.do?bbsSn=175745&step1=3&step2=1
  14. Son, T. (2022). Development and application of intelligent tutoring system applying a cognitive diagnosis model: Focused on number and operation area in middle school [Unpublished doctoral dissertation, Graduate School of Korea National University of Education].
  15. Shin, D. (2020a). Artificial intelligence in primary and secondary education: A systemic review. Journal of Educational Research in Mathematics, 30(3), 531-552. https://doi.org/10.29275/jerm.2020.08.30.3.531
  16. Shin D. (2020b). 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
  17. Youn, J., Kang, M., & Kim, E. (2013). A relationship among task value, academic self-efficacy, motivation, self-regulated learning and academic procrastination in a college e-learning course. The Journal of Korean association of computer education, 16(1), 81-95. https://doi.org/10.32431/KACE.2013.16.1.009
  18. Lee, J., & Kwon, S. (2021). EduTech's Current Issues, Overcoming Strategies, and Prospects. The Magazine of the IEIE, 48(4), 44-51.
  19. Lee, J. H., & Huh, N. (2020). Developing adaptive math learning program using artificial intelligence. East Asian Mathematical Journal, 36(2), 273-289. https://doi.org/10.7858/EAMJ.2020.018
  20. Lim, K. Y., Kim, H. J., Choi, J., & Kim, Y. J. (2020). Usability test of learner dashboard: Visualization of multi-dimensional interaction information. Journal of Korean Association for Educational Information and Media, 26(2), 395-424. https://doi.org/10.15833/KAFEIAM.26.2.395
  21. Lim, K., Eun, J., Jung, Y., & Park, H. (2018). Exploratory study on the information design of online dashboard for learner-centered learning. The Journal of Korean Association of Computer Education, 21(3), 35-50. https://doi.org/10.32431/KACE.2018.21.3.004
  22. Lim, K. Y., Lim, J. Y., Kim, Y. J., Jin, M. H., & Park, M. J. (2017). Developing Criteria for Evaluating the Quality of Online Dashboard: From HCI Perspective. The Journal of Korean Association of Computer Education, 23(4), 861-889.
  23. Lim, K. Y., Lim, J. Y., & Jin, M. (2021). A systematic literature review of technology-based personalized learning: Research from 2011-2020 in Korea. Journal of Educational Technology, 37(3), 525-559. https://doi.org/10.17232/KSET.37.3.525
  24. Yim, M, Kim, H. M., Nam, J., & Hong, O. (2021). Exploring the application of elementary mathematics supporting system using arificial intelligence in teaching and learning. Journal of Korea Society Educational Studies in Mathematics, 23(2), 251-270.
  25. 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 Toctoc math expedition in Korea. The Journal of Korea Elementary Education, 32(4), 61-73.
  26. Lim, W., & Park, M. (2021). AI-based mathematics education: A review of issues in international research. Journal of Learner-Centered Curriculum and Instruction, 21(14), 621-635.
  27. 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.
  28. Chung, Y. K., & Kim, J. (2018). Factors to Disturb Adult Learner's e-Learning Persistence:A Case Study of H-Cyber University in Seoul Korea. Journal of Digital Convergence, 16(12), 109-122. https://doi.org/10.14400/JDC.2018.16.12.109
  29. Jho, H. (2021). The Directions and Challenges of Science Education Based on the Prediction of Future Education and Schools. Journal of Research in Curriculum Instruction, 25(1), 61-78. https://doi.org/10.24231/RICI.2021.25.1.61
  30. Joo, Y. J., Jang, M. J., & Lee, H. J. (2007). An In-depth Analysis of Dropout Factors based on Cyber University Student's Dropout Experiences. Journal of Korean Association for Educational Information and Media, 13(3), 209-233.
  31. Choi, S. (2021). Artificial Intelligence in Education: A Literature Review on Education Using Artificial Intelligence. The Journal of Korean Association of Computer Education, 24(3), 11-21. https://doi.org/10.32431/KACE.2021.24.3.002
  32. Han, S. J., Kim, H. W., & Ko, H. K. (2022). A Case Analysis for Learning Management Systems that support Individual Students' Mathematics Learning. East Asian Mathematical Journal, 38(2), 187-214. https://doi.org/10.7858/EAMJ.2022.013
  33. Heo, G. E., & Lee, D. S. (2021). A Phenomenological Study on Non-Face-to-Face Learning Experience of Elementary School Students. Journal of Education & Culture, 27(4), 495-520. https://doi.org/10.24159/JOEC.2021.27.4.495
  34. Arsarkij, J., & Laohajaratsang, T. (2021). A Design of Personal Learning Network on Social Networking Tools with Gamification for Professional Experience. International J ournal of Emerging Technologies in Learning (iJET), 16(18), 53-68. https://doi.org/10.3991/ijet.v16i18.25159
  35. Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 13(6), 4-16. https://doi.org/10.3102/0013189X013006004
  36. Christopoulos, A., Kajasilta, H., Salakoski, T., & Laakso, M. J. (2020). Limits and virtues of educational technology in elementary school mathematics. Journal of Educational Technology Systems, 49(1), 59-81. https://doi.org/10.1177/0047239520908838
  37. Dabbagh, N., & Castaneda, L. (2020). The PLE as a framework for developing agency in lifelong learning. Educational Technology Research and Development, 68(6), 3041-3055. https://doi.org/10.1007/s11423-020-09831-z
  38. Dalgarno, B., & Lee, M. J. (2010). What are the learning affordances of 3-D virtual environments?. British Journal of Educational Technology, 41(1), 10-32. https://doi.org/10.1111/j.1467-8535.2009.01038.x
  39. Darvishi, A., Khosravi, H., Sadiq, S., & Gasevic, D. (2022). Incorporating AI and learning analytics to build trustworthy peer assessment systems. British Journal of Educational Technology, 53(4), 844-875. https://doi.org/10.1111/bjet.13233
  40. Edson, A. J., & Phillips, E. D. (2021). Connecting a teacher dashboard to a student digital collaborative environment: Supporting teacher enactment of problem-based mathematics curriculum. ZDM- Mathematics Education, 53(6), 1285-1298. https://doi.org/10.1007/s11858-021-01310-w
  41. Harding, A., & Engelbrecht, J. (2015). Personal learning network clusters: A comparison between mathematics and computer science students. Journal of Educational Technology & Society, 18(3), 173-184.
  42. Holmes, W., Bialik, M., & Fadel, C. (2020). Artificial intelligence in education. Center for Curriculum Redesign. 정제영, 이선복 역. 인공지능 시대의 미래교육. 박영스토리. (원저 출판 2019년)
  43. Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
  44. Ivanova, M. (2009). From personal learning environment building to professional learning network forming. In Conference proceedings of» eLearning and Software for Education «(eLSE) (Vol. 5, No. 01, pp. 27-32). Carol I National Defence University Publishing House.
  45. Kashive, N., Powale, L., & Kashive, K. (2021). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/IJILT-05-2020-0090
  46. Miller, M. L., & Goldstein, I. P. (1977, August). Structured planning and debugging. In Proceedings of the 5th international joint conference on Artificial Intelligence, 2, 773-779.
  47. Montebello, M. (2021). AI Injected e-Learning:쏟 Future of Online Education. Springer International Publishing. 임유진, 홍유나, 김세영, 김보경 역. 미래의 온라인 교육. AI기반 e-러닝과 개인화 학습. 박영스토리. (원저 출판 2018년)
  48. Root, W. B., & Rehfeldt, R. A. (2021). Towards a modern-day teaching machine: the synthesis of programmed instruction and online education. The Psychological Record, 71(1), 85-94. https://doi.org/10.1007/s40732-020-00415-0
  49. Shin, D. (2021). Teaching mathematics integrating intelligent tutoring systems: Investigating prospective teachers' concerns and TPACK. International J ournal of Science and Mathematics Education, 1-18.
  50. Skinner, B. F. (1968). The technology of teaching. Appleton-Century-Crofts.
  51. Sahin, S., & Uluyol, C. (2016). Preservice teachers' perception and use of personal learning environments (PLEs). International Review of Research in Open and Distributed Learning, 17(2), 141-161.
  52. The Telegraph (2020. 3. 5.). 'AI teachers' to be offered to British students off school due to coronavirus. Retrieved from https://www.telegraph.co.uk/technology/2020/03/05/ai-teachers-offered-british-students-school-due-coronavirus/
  53. U. S. Department of Education (2017). Reimagining the role of technology in education: 2017 national education technology plan update. Retrieved from https://tech.ed.gov/files/2017/01/NETP17.pdf
  54. Vandewaetere, M., & Clarebout, G. (2014). Advanced technologies for personalized learning, instruction, and performance. In M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop. (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 425-437).
  55. Walkington, C., & Bernacki, M. (2019). Personalizing algebra to students' individual interests in an intelligent tutoring system: Moderators of impact. International Journal of Artificial Intelligence in Education, 29(1), 58-88. https://doi.org/10.1007/s40593-018-0168-1
  56. Watson, W. R., & Watson, S. L. (2017). Principles for personalized instruction. In C. M. Reigeluth, B. J. Beatty, & R. D. Myers (Eds.), Instructional-design theories and models: The learner-centered paradigm of education (vol. 4, pp. 93-120).