$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

국내 AI 수학 학습 플랫폼의 적응형 학습에 대한 분석
Analysis of adaptive learning in Korea's AI mathematics learning platforms

韓國學校數學會論文集 = Journal of the Korean school mathematics society, v.26 no.3, 2023년, pp.245 - 268  

이기마 (고려대학교) ,  이유정 (고려대학교) ,  김희정 (고려대학교)

초록
AI-Helper 아이콘AI-Helper

최근 인공지능(AI) 기술의 발전과 더불어, AI를 활용한 맞춤형 교육의 필요성이 강조되고 있다. 특히, AI 디지털 교과서의 개발과 실행을 앞두고, 개별화 맞춤형 교육에 대한 연구자, 정책 입안자, 개발자, 실행자 및 사용자 간의 공유된 이해 차이가 존재하며, 이는 개발의 효율성과 실행의 효과성에 영향을 미칠 수 있다. 본 연구에서는 체계적 문헌 검토를 통해 AI 수학 디지털교과서에 필요한 필수적인 적응적 기능을 도출하고, 현재 국내 AI 수학 학습 플랫폼의 적응형 학습 제공의 형태를 분석하였다. 분석 결과, 국내 AI 수학 학습 플랫폼에서는 정서와 동기 적응성이나 메타인지 적응성에 비해 지식 적응성에 크게 집중하고 있었다. 또한 적응 방법과 관련해서는 설계와 과제 루프 측면 보다는 단계 루프 측면이 많이 반영이 되었다. 즉, 학습 플랫폼의 설계와 업데이트, 학습 데이터를 바탕으로 한 맞춤형 과제 제공, 사전 진단 및 실시간 모니터링을 통한 문제 난이도 조절 등과 같은 설계적 적응 방법이나 과제적 적응 방법이 잘 나타나지 않았다. 본 연구의 결과는 AI 수학 디지털교과서를 개발하고 있는 이 시점에서 연구자와 정책 입안자, 개발자 들이 협력적으로 학생들의 전략, 오류, 학습 스타일, 메타인지, 협력적 학습과 같은 다양한 적응 대상과 방법을 고려하여 학생들에게 더 풍부한 개별화 맞춤형 학습 경험을 제공할 수 있도록 더 깊이 있게 연구하고, 이에 대한 국가적 담론과 정책 방향을 구성해야 함을 시사한다.

Abstract AI-Helper 아이콘AI-Helper

With the recent advancements in Artificial Intelligent (AI) technology, there's an emphasized need for adaptive and individualized education utilizing AI. Especially as the national approach to the development and implementation of AI digital textbooks, there exists a discrepancy in the shared under...

주제어

참고문헌 (76)

  1. 경상남도교육청(2021). 경남형 빅데이터.인공지능(AI) 기반 지능형 교육지원시스템 구축 심층 연구. 경상남도교육청. 

  2. 교육부(2023. 2. 23). 모두를 위한 맞춤 교육의 실현 디지털 기반 교육혁신 방안. https://www.moe.go.kr/boardCnts/viewRenew.do?boardID294&boardSeq94011&lev0&searchTypenull&statusYNW&page1&smoe&m020402&opTypeN 

  3. 교육부(2023. 6. 8). AI 디지털교과서로 1:1 맞춤 교육시대 연다. https://www.moe.go.kr/boardCnts/viewRenew.do?boardID294&boardSeq95261&lev0&searchTypenull&statusYNW&page1&smoe&m020402&opTypeN 

  4. 관계부처 합동(2020). 인공지능시대 교육정책방향과 핵심과제 대한민국의 미래 교육이 나아가야 할 길. 관계부처 합동. 

  5. 교육부(2021. 1. 26). 2021 업무계획 보도자료. https://moe.go.kr/boardCnts/view.do?boardID72713&boardSeq83340&lev0&searchTypenull&statusYNW&page1&smoe&m0311&opTypeN 

  6. Kim, M., & Yoo, Y. (2022). The effect of TOEIC classes applying artificial intelligence-based adaptive learning on academic achievement and Influencing factors. Journal of Learner-Centered Curriculum?and Instruction, 22(23), 267-280. https://doi.org/10.22251/jlcci.2022.22.23.267 

  7. Kim, S. (2003).?A study on developing the teachers' guide book for diagnosis and prescription of students' mathematical errors.?Journal of Korea Society of Educational Studies in Mathematics, 5(2), 209-221. 

  8. Kim, H., Ko, E., Lee, D., Cho, J., Cho, H., Choi, J., Han, C., & Hwang,?J. (2020). Developing assessment items to diagnose elementary mathematics learning difficulties. Korea Foundation?for the Advancement of Science and Creativity, Research Report BD21010011. 

  9. Kim, H., Han, C., Bae, M., & Kwon, O. (2017). The relationship?between mathematics teachers' noticing and responsive teaching: In the context of teaching for all students'?mathematical thinking. The Mathematical Education, 56(3), 341-363. https://doi.org/10.7468/mathedu.2017.56.3.341 

  10. Kim, S. C. (2021). A functional analysis of mathematics learning support platform - Including?utilization of AI -. Asia-pacific Journal of Convergent Research Interchange, 7(11), 315-326. https://doi.org/10.47116/apjcri.2021.11.26 

  11. Park, M. G. (2020). Applications?and possibilities of artificial intelligence in mathematics education. Korean Society of Mathematical Education,?34(4), 545-561. 

  12. Park,?H. Y., Son, B. E., & Ko, H. K. (2022). Study on the mathematics teaching and learning artificial intelligence?platform analysis. Korean Society of Mathematical Education, 36(1), 1-21. https://doi.org/10.7468/jksmee.2022.36.1.1 

  13. Bang,?D. I., & Yoon, H. J. (2022). Investigation of primary & secondary school teachers' trust on AI-based educational?technology. Educational Research, 85, 227-247. https://doi.org/10.17253/swueri.2022.85..012 

  14. 서경원, 진성희, 유미나, 이현진, 이한솔(2022). 인공지능 기반 맞춤형 교육서비스 지원 방안 연구. 서울특별시교육청교육연구정보원 연구보고 서교연 2022-31. 

  15. 안성훈, 차현진, 주길홍, 안석훈, 김현진, 윤종현, 김성혜, 안경진, 이춘식, 곽광호, 김주연, 변자정, 최준석, 이정환, 이정태?(2023). 2022 개정 교육과정에 따른 디지털교과서 개선 방안. 한국교육학술정보원 연구보고 KR 2023-01. 

  16. 안성훈, 차현진(2023). AI 디지털교과서 도입을 위한 쟁점 분석 및 개발 전략. 한국교육학술정보원 연구보고 RM 2023-11. 

  17. Lee,?S. H., & Kim, H. S. (2021). The study of development and implementation of adaptive learning design model?in Chinese language education. Chinese Language Education, 35, 41-66. https://doi.org/10.24285/CLER.2021.11.35.41? 

  18. Ee, J. H., & Huh, N. (2020). Developing adaptive math learning program using artificial intelligence. East?Asian Mathematical Journal, 36(2), 273-289.? 

  19. Lim, S. T., & Kim, E. H. (2017). The design of dashboard for instructor feedback support based on learning?analytics. The Journal of Korean Association of Computer Education, 20(6), 1-15. https://doi.org/10.32431/kace.2017.20.6.001 

  20. 임철일, 계보경, 최미애, 이웅기, 이재홍, 배유진, 송유경, 정혜원(2021). 포스트 코로나 시대의 학습 환경 연구. 한국교육학술정보원 연구보고 RR 2021-3. 

  21. Jang, S. Y., & Ahn, B. G. (2010). Effective teaching method for errors patterns in numbers and operations?of elementary mathematics. Journal of Elementary Mathematics Education in Korea, 14(2), 355-376. 

  22. 정영식, 서순식, 조순옥, 서정희(2022). 교육 격차 해소를 위한 디지털 기술 적용 방안 연구. 한국교육학술정보원 연구보고?KR 2022-4. 

  23. 주정흔, 김보경, 김아람, 임유진, 임세범, 이예지(2022). 개별 맞춤형 인공지능(AI) 활용교육의 가능성과 과제: 'AI 튜터마중물학교' 운영 사례를 중심으로. 서울특별시교육청교육연구정보원 연구보고 서교연 2022-77. 

  24. Cho, H. M., Lee, H. J., Lee, G. M., &?Kim, H. J. (2022). How do Korean and U.S. elementary preservice teachers analyze students' addition and?subtraction computational strategies and errors? Journal of the Korean School Mathematics Society, 25(4), 423-446.?https://doi.org/10.30807/ksms.2022.25.4.006 

  25. Cha, E. J. (2022).?A meta-analysis of the effectiveness of AI-based adaptive learning systems. Doctoral dissertation, Ewha Womans?University. 

  26. Choi, S. H., Kim, D. J., & Shin, J. H. (2013). Analysis on characteristics of university students'?problem-solving processes based on mathematical thinking styles. Journal of Educational Research in Mathematics,?23(2), 153-171. 

  27. 한국교육학술정보원(2022). 국내외 AI 튜터 활용 사례. 한국교육학술정보원 연구보고 심층호 제5호. 

  28. Acampora, G., Gaeta, M., & Loia, V. (2011). Combining multi-agent paradigm and memetic computing for personalized?and adaptive learning experiences. Computational Intelligence, 27(2), 141-165. https://doi.org/10.1111/j.1467-8640.2010.00367.x 

  29. Aleven, V., & Koedinger, K. R. (2013). Knowledge component approaches to learner modeling. In R. Sottilare, A.?Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for adaptive intelligent tutoring systems (Vol. I,?pp. 165-182). US Army Research Laboratory. 

  30. Aleven, V., & Koedinger, K. R. (2000). Limitations of student control: Do students know when they need help? In?G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Proceedings of the 5th international conference on intelligent tutoring?systems (pp. 292-303). Springer. 

  31. Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning?technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed.,?pp. 522-560). Routledge. 

  32. Aleven, V., Sewall, J., Popescu, O., Xhakaj, F., Chand, D., Baker, R., Wan, Y., Siemens, G., Rose, C., & Gasevic,?D. (2015). The beginning of a beautiful friendship?: Intelligent tutoring systems and MOOCs. In C. Conati,?N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial intelligence in education: 17th international conference,?AIED 2015 (Vol. 9112, pp. 525-528). Springer. https://doi.org/10.1007/978-3-319-19773-9_53 

  33. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal?of the Learning Sciences, 4(2), 167-207. 

  34. Beck, J. E., Woolf, B. P., & Beal, C. R. (2000) ADVISOR: A machine learning architecture for intelligent tutor?construction. In H. Kautz & B. Porter (Eds.), Proceedings of the Seventeenth National Conference on Artificial?Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (pp. 552-557). The AAAI?Press. 

  35. Boekaerts, M. (2007). Understanding students' affective processes in the classroom. In P. A. Schutz & R. Pekrun (Eds.),?Emotion in education (pp. 37-56). Elsevier Academic Press. https://doi.org/10.1016/B978-012372545-5/50004-6 

  36. Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic?literature review process within the software engineering domain. The Journal of Systems and Software, 80(4),?571-583. https://doi.org/10.1016/j.jss.2006.07.009 

  37. Brown, M., Brown, P., & Bibby, T. (2008). "I would rather die": Reasons given by 16-year-olds for not continuing?their study of mathematics. Research in Mathematics Education, 10(1), 3-18. https://doi.org/10.1080/14794800801915814 

  38. Brusilovsky, P., & Millan, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P.?Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 3-53). Springer. 

  39. Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. (2020). Constructing a design framework and pedagogical?approach for adaptive learning in higher education: A practitioner's perspective. The International Review of?Research in Open and Distributed Learning, 21(1), 173-197. 

  40. Corbett, A., McLaughlin, M., & Scarpinatto, K. C. (2000). Modeling student knowledge: Cognitive tutors in high school?and college. User Modeling and User-Adapted Interaction, 10, 81-108. https://doi.org/10.1023/A:1026505626690 

  41. D'Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system.?International Journal Human-Computer Studies, 70(5), 377-398. https://doi.org/10.1016/j.ijhcs.2012.01.004 

  42. D'Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., & Graesser, A. (2010). A time for?emoting: When affect-sensitivity is and isn't effective at promoting deep learning. In J. Kay & V. Aleven (Eds.),?Proceedings of the 10th international conference on intelligent tutoring systems (pp. 245-254). Springer. 

  43. Dochy, F., Segers, M., & Pletinckx, J. (2002). The question of entry assessment of how can we assess previously?acquired knowledge? Main research findings and implications for practice. Journal of Continuing Engineering?Education and Life, 12(1), 31-44. https://doi.org/10.1504/IJCEELL.2002.000420 

  44. Dodds, P., & Fletcher, J. D. (2004). Opportunities for new smart learning environments enabled by next generation?web capabilities. Journal of Educational Multimedia and Hypermedia, 13(4), 22. 

  45. Duncan, A. (2013, December, 17). Enabling the future of learning. https://www.whitehouse.gov/blog/2013/12/17/enabling-future-learning 

  46. Essa, A. (2016). A porssible future for next generation adaptive learning systems. Smart Learning Environments, 3(16),?1-24. 

  47. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American?Psychologist, 34(10), 906-911. https://doi.org/10.1037/0003-066X.34.10.906 

  48. Forbes-Riley, K., & Litman, D. (2011). Benefits and challenges of real-time uncertainty detection and adaptation in?a spoken dialogue computer tutor. Speech Communication, 53(9-10), 1115-1136. https://doi.org/10.1016/j.specom.2011.02.006 

  49. Ford, N., & Chen, S. Y. (2001). Matching/mismatching revisited: An empirical study of learning and teaching styles.?British Journal of Educational Technology, 32(1), 5-22. https://doi.org/10.1111/1467-8535.00173 

  50. Goguadze, G., Sosnovsky, S., Isotani, S., & McLaren, B. M. (2011). Evaluating a Bayesian student model of decimal?misconceptions. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings?of the 4th International Conference on Educational Data Mining (EDM 2011) (pp. 301-306). International Educational?Data Mining Society. 

  51. Harida, M., Gutjahr, G., Raman, R., Ramaraju, R., & Nedungadi, P. (2020). Predicting school performance and early?risk of failure from an intelligent tutoring system. Education and Information Technologies, 25(5), 3995-4013.?https://doi.org/10.1007/s10639-020-10144-0 

  52. Heilman, M., Collins-Thompson, K., Callan, J., Eskenazi, M., Juffs, A., & Wilson, L. (2010). Personalization of reading?passages improves vocabulary acquisition. International Journal of Artificial Intelligence in Education, 20(1), 73-98.?https://doi.org/10.3233/JAI-2010-0003 

  53. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016?higher educational edition. The New Media Consortium. 

  54. Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal?of Educational Psychology, 96(3), 558-568, http://dx.doi.org/10.1037/0022-0663.96.3.558 

  55. Kerr, P. (2015). Adaptive Learning. ELT Journal, 70(1), 88-93. https://doi.org/10.1093/elt/ccv055 

  56. Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing systematic literature review in software engineering.?Keele University and Durham University. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf 

  57. Koedinger, K. R., Brunskill, E., S.J.d. Baker, R., McLaughlin, E. A., & Stamper, J. C. (2013). New potentials for data-driven?intelligent tutoring system development and optimization. AI Magazine, 34(3), 27-41. https://doi.org/10.1609/aimag.v34i3.2484 

  58. Koedinger, K. R., & Nathan, M. J. (2004). The real story behind story problems: Effects of representations on quantitative?reasoning. The Journal of the Learning Sciences, 13(2), 129-164. 

  59. Lee, G. M., Hwang, J. H., & Kim, H. J. (2023). A critical exploration of mathematics learning trajectories research?in the Korean context: A systematic literature review. Journal of Educational Research in Mathematics, 33(3),?721-744. https://doi.org/10.29275/jerm.2023.33.3.721 

  60. Long, Y., & Aleven, V. (2013). Skill diaries: Improve student learning in an intelligent tutoring system with periodic?self-assessment. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th international?conference on artificial intelligence in education AIED 2013 (pp. 249-258). Springer. https://doi.org/10.1007/978-3-642-39112-5_26 

  61. Lovett, M., Meyer, O., & Thille, C. (2008). The open learning initiative: Measuring the effectiveness of the OLI statistics?course in accelerating student learning. Journal of Interactive Media in Education, 2008(1), 1-16. https://doi.org/10.5334/2008-14 

  62. Liu, C. C., Liao, M. G., Chang, C. H., & Lin, H. M. (2022). An analysis of children' interaction with an AI chatbot?and its impact on their interest in reading. Computers & Education, 189. https://doi.org/10.1016/j.compedu.2022.104576 

  63. Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive?tutoring. Educational Psychologist, 40(4), 257-265. https://doi.org/10.1207/s15326985ep4004_7 

  64. Maurer, S. B. (1987). New knowledge about errors and new views about learners: What they mean to educatiors?and more educators would like to know. In Schenfel, A. H. (Ed.), Cognitive science and mathematics education?(pp. 165-189). Routledge. 

  65. McLaren, B. M., Adams, D., Durkin, K., Goguadze, G., Mayer, R. E., Rittle-Johnson, B., Sosnovsky, S., Isotani, S., &?van Velsen, M. (2012). To err is human, to explain and correct is divine: A study of interactive erroneous?examples with middle school math students. In Ravenscroft, A., Lindstaedt, S., Delgado Kloos, C., & Hernandex-Leo, D. (Eds.), Proceedings of ECTEL 2012: seventh European conference on technology enhanced learning, LNCS?7563 (pp. 222-235). Springer. 

  66. Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent?tutoring system. Computers & Education, 60(1), 264-272. https://doi.org/10.1016/j.compedu.2012.07.002 

  67. Montebello, M. (2018). AI injected e-learning. Springer. 

  68. NCTM (2000). Principles and standards for school mathematics. NCTM. 

  69. Sharma, P., & Harkishan, M. (2022). Designing an intelligent tutoring system for computer programing in the Pacific.?Education and Information Technologies, 27(5), 6197-6209. https://doi.org/10.1007/s10639-021-10882-9 

  70. Schoenfeld, A. H. (1985). Mathematical problem solving. Academic Press. 

  71. Smith, M. S., & Stein, M. K. (2011). 5 practice for orchestrating productive mathematics discussion. NCTM. 

  72. Southwell, R., Pugh, S., Perkoff, E. M., Clevenger, C., Bush, J., Lieber, R., Ward, W., Foltz, P., & D'Mello, S. (2022).?Challenges and feasibility of automatic speech recognition for modeling student collaborative discourse in classrooms.?International Educational Data Mining Society. 

  73. UNESCO IITE (2012). Personalized learning: A new ICT-enabled education approach. UNESCO IITE. http://iite.unesco.org/pics/publications/en/files/3214716.pdf 

  74. US Department of Education Office of Educational Technology (2010). National education technology plan 2010:?Transforming American Education: Learning powered by technology. US Department of Education. https://www.ed.gov/sites/default/files/netp2010.pdf 

  75. Walkington, C. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact?of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932-945.?https://doi.org/10.1037/a0031882 

  76. Zhou, G., Moulder, R., Sun, C., & D'Mello, S. (2022). Investigating temporal dynamic underlying successful collaborative?problem solving behaviors with multilevel vector autoregression. In A. Mitrovic & N. Bosch (Eds.), Proceedings?of the 15th International Conference on Educational Data Mining (pp. 290-301). International Educational Data?Mining Society.? 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로