The Mathematical Education (한국수학교육학회지시리즈A:수학교육)

Korean Society of Mathematical Education (한국수학교육학회)
권호 표지
DOI QR코드

DOI QR Code

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

머신러닝 추천모듈이 적용된 맞춤형 학습 플랫폼 효과성 탐색: 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도를 중심으로

  • Received : 2020.11.02
  • Accepted : 2020.11.24
  • Published : 2020.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to verify the effects of personalized learning platforms applied with machine learning recommendation modules that upgrade recommended algorithms by themselves through learning big data analysis on students' learning time, self-directed learning ability, mathematics achievement, and attitudes toward mathematics, and the correlation between them. According to the study, customized learning affected learning time, self-directed learning ability and mathematics attitude, while learning time affected self-directed learning ability. Self-directed learning ability has had a significant impact on the attitude of mathematics and mathematical achievements. As a result of the mediated effectiveness test, the indirect impact of customized learning on mathematics attitude and mathematics performance was significant through the medium of learning time and self-directed learning ability.

본 연구의 목적은 학습 빅데이터 분석을 통해 추천 알고리즘을 스스로 고도화하는 머신러닝 추천모듈이 적용된 개인 맞춤형 학습 플랫폼이 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 미치는 영향과 이들 사이의 구조적 관계를 검증하는 것이다. 연구 결과 개인 맞춤형 학습은 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 대해 긍정적인 영향을 미치고 있었다. 또한, 맞춤형 학습과 수학에 대한 태도와 수학학업성취도의 관계에서 학습시간과 자기주도적 학습능력의 매개효과가 유의하였다.

Keywords

References

  1. An, J. S. (2014). The effect of academic achievement and cooperative learning attitudes via differentiated cooperative learning in a class. Journal of the Korean School Mathematics Society, 17(4), 465-492.
  2. Aypay, A. (2010). Information and communication technology(ICT) usage and achievement of Turkish students in PISA 2006. The Turkish Online J ournal of Educational Technology, 9(2), 16-124.
  3. Baik, K. S. (2011). The effects of self-directed learning program on self-directed learning ability, problem solving, and academic achievement for college students. The Journal of Education Research, 32(1), 77-95.
  4. Bennett, S., & Lockyer, L. (1999). The impact of digital technologies on teaching and learning in K-12 education. Curriculum Corporation.
  5. Cechinel, C., Sicilia, M. A., SaNchez-Alonso, S., & GarciA-Barriocanal, E. (2013). Evaluating collaborative filtering recommendations inside large learning object repositories. Information Processing & Management, 49(1), 34-50. https://doi.org/10.1016/j.ipm.2012.07.004
  6. Choi, J., & Jeong, J. (2019). Learning analysis technology applied to adaptive learning situation. Academic Conference Report of the Korean Educational Engineering Society, 2019(1), 2015-2021.
  7. Chung, Y. K. et al. (2017). Differential relations of students' perceived instructions to their motivation, classroom attitude, and academic achievement: The moderating role of self-efficacy. The Korean Journal of Educational Methodology Studies, 29(1), 211-235. https://doi.org/10.17927/tkjems.2017.29.1.211
  8. Chunjae Education (2020). Introduction of Dr. Math. Seoul: Chunjae Education.
  9. Clark, R. C. (1988). Metacognition and human performance improvement. Performance Improvement Quarterly, 1(1), 33-45. https://doi.org/10.1111/j.1937-8327.1988.tb00005.x
  10. EBS. (2020). An analysis on the EBS eSOF AI education service expansion and improvement tasks. EBS AI training case analysis. EBS.
  11. Garcia-Valdez, M, et al. (2009). A hybrid recommender system architecture for learning objects. Berlin Heidelberg: Springer-Verlag.
  12. Go, S. (2013). Does private tutoring substitute for self-motivated study?, The Korea International Economic Association, 15(1), 5-36.
  13. Han, H. J., Choi, Y. S., & Choi, S. P. (2018). A study on personalization of science and technology information by user interest tracking technique. Journal of The Korean Society for Library and International Science, 52(3), 5-33.
  14. Han, J. W., Jo, J. C., & Lim, H. S. (2018). Development of personalized learning course recommendation model for ITS. Journal of the Korea Convergence Society, 9(10), 21-28. https://doi.org/10.15207/JKCS.2018.9.10.021
  15. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational psychologist, 41(2), 111-127. https://doi.org/10.1207/s15326985ep4102_4
  16. Jeong, H. Y., Lee, Y. H., & Hong, B. H. (2010). The propose system of learning contents using the preference of learner. The Journal of the Korea Contents Association, 10(1), 477-485. https://doi.org/10.5392/JKCA.2010.10.1.477
  17. Jin, H. B., Lee, S. A., & Kim, H. S. (2003). Effects of metacognitive knowledge and working memory on task performance requiring metacognitive regulation skills. Journal of Korean Association for Educational Information and Broadcasting, 9(3), 119-148.
  18. Kim, D. K. (2017). Personal recommendation service design through big data analysis on science technology information service platform. The Korean Biblia Society For Library and Information Science, 28(4), 501-518.
  19. Kim, H. S. (2010). Analysis of the effects of private education on academic achievement and labor market performance. KDI. report 2010-05.
  20. Kim, J. H., Kim, J. A., & Kang, S. W. (2006). Analysis of factors and type of high school students' self-directed learning ability. The Korean Journal of Educational Methodology Studies, 18(2), 221-237. https://doi.org/10.3946/kjme.2006.18.3.221
  21. Kim, K. (2020). Study on the Effects and Limitations of Blended Learning-based College Lectures in Online Courses. Korean Journal of General Education, 14(5), 239-249. https://doi.org/10.46392/kjge.2020.14.5.239
  22. Kim, S. R., Nam, C, W., & Jang, S. Y. (2012). The effects of students' EBS program and computer use, and teachers' ICT use in education on the students' academic achievements. Journal of Educational Technology, 28(4), 849-877. https://doi.org/10.17232/KSET.28.4.849
  23. Kim, W. S. (2019). Content recommendation techniques for personalized software education. Journal of Digital Convergence, 17(8), 95-104. https://doi.org/10.14400/JDC.2019.17.8.095
  24. Knowles, M. S. (1975). Self-directed learning: a guide for learners and teachers. Chicago. IL: Follett.
  25. Ku, J. H., & Kim, K. (2017). A study on the intelligent adaptive learning for communication education in smart education environment. Journal of Engineering Education Research, 20(3), 25-31. https://doi.org/10.18108/jeer.2017.20.3.25
  26. Learning Spark. (2020). Case study for shrinkage of AI-based EBSMath learning service model. EBS Creative Convergence Project Report (2020. 6. 29).
  27. Lee, D. H., & Lee, G. H. (2001). The effects of self-regulated learning strategies using WEB on students' academic achievements and learning attitudes in the middle school mathematics: Focused on the chapter "Function" of the first grade. Journal of the Korean School Mathematics Society, 4(2), 75-84.
  28. Lee, H. J., Choi, J. W., & Boo, E. J. (2019). The effect of situational interest enhancing strategies on mathematics learning in elementary school students. The Korean Journal of Educational Psychology, 33(4), 681-706. https://doi.org/10.17286/KJEP.2019.33.4.07
  29. Lee, J. M., & Lee, G. J. (2014). Analysis influence factors of university education satisfaction and learning performance using NASEL 2014. The Korean Educational Research Association Annual Academic Conference.
  30. Lee, J. M., & Lee, G. J. (2017). A study on the factors associated with self-directed learning ability of college students. Korean Journal of Educational Administration, 35(3), 133-153.
  31. Lee, K. Y. (2020). After the introduction of deep learning, the market is now dominated by big data rather than expertise. Available from https://dbr.donga.com/article/view/1303/article_no/9367
  32. Lee, T. H., Shen, P. D., & Tsai, C. W. (2008). Enhancing computing skills of low-achieving students via e-learning: A design experiment of web-based, problem-based learning and self-regulated learning. Cyberpsychology & Behavior, 11(4), 431-436. https://doi.org/10.1089/cpb.2007.0080
  33. Lim, M. J. & Kim, S. I. (2006). The effects of Comics on Interest and Achievement in Science. The Korean Journal of Educational Psychology. 20(3), 549-569.
  34. Ma, X. (1997). Reciprocal relationships between attitude toward mathematics and achievement in mathematics. The Journal of Educational Research, 90(4), 221-229. https://doi.org/10.1080/00220671.1997.10544576
  35. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: Th next frontier for innovation, competition, and productivity. McKinsey Global Institute, Seattle, May 2011.
  36. Middleton, J. A., & Spanias, P. A. (1999). Motivation for achievement in mathematics: Findings, generalizations, and criticisms of the research. Journal for research in Mathematics Education, 65-88.
  37. Ministry of Education. (2020a). A comprehensive plan for science, mathematics, information and convergence education. Ministry of education.
  38. Ministry of Education. (2020b). Evaluation of academic achievement: Achievement standards for 3rd grade middle school curriculum (under-standard of basic education). Ministry of education.
  39. Nam, Y. M. & Rim, S. H. (2000). The influence of the student-centered classes on the change in the mathematical attitude. Journal of the Korean School Mathematics Society, 3(2), 37-46.
  40. Organization for Economic Cooperation and Development[OECD]. (2019). PISA 2018: Insights and interpretations. Available from https://www.oecd.org/pisa/PISA%202018%20Insights%20and%20Interpretations%20 FINAL%20PDF.pdf
  41. Park, D., Ahn, J., Jang, J., Yu, W., Kim, W., Bae, Y., & Yoo, I. (2020). The development of software teaching-learning model based on machine learning platform. Journal of the Korean Association of Information Education, 24(1), 49-57. https://doi.org/10.14352/jkaie.2020.24.1.49
  42. Park, J. W., & Lee, Y. K. (2011). A method for recommending learning contents using similarity and difficulty. Journal of The Korea Society of Computer and Information, 16(7), 127-135. https://doi.org/10.9708/jksci.2011.16.7.127
  43. Park, S. H. (2015). Impact of learning communities on academic self-efficacy, self-directed learning, communication, and cooperation abilities. Lifelong Learning Society, 11(4), 115-136. https://doi.org/10.26857/JLLS.2015.11.11.4.115
  44. Rakover, S. S., & Cahlon, B. (2001). Face recognition: Cognitive and computational processes (Vol. 31). John Benjamins Publishing.
  45. Song, Y. A., Kang, S. J.. (2014). The Analysis on the Effects of Differentiated Science-Subject Classroom Instruction on the Academic Achievement and Science Learning Attitude in General High School. Journal of Cheongram Science Educational Studies, 20(1), 1-12.
  46. Sung, E. M. (2011). Structural equation model analyzing relationships of visualization tendency, learning attitude for the subject, and academic ability for primary school students. The Journal of Elementary Education, 24(3), 27-50.
  47. Sung, E. M., Chae, Y. J., & Lee, S. H. (2018). Analysis of types and characteristics of self-directed learning of learners in online software education. The Journal of Korean Association of Computer Education, 22(1), 31-46. https://doi.org/10.32431/kace.2019.22.1.004
  48. Squirrel AI Learning. (2020). 1st AI-powered adaptive education provider in China. Available from http://squirrelai.com/
  49. Time Education. (2020). Botami. Internal report of Time Education. Seoul: Time Education.
  50. TIMSS and PIRLS International Study Center (2016). TIMSS 2015 international report. Available from http://timssandpirls.bc.edu/timss2015/international-results/timss-2015/mathematics/student-achievement/
  51. Tuomi, I. (2019). The impact of artificial intelligence on learning, teaching, and education: Policies for the future. JRC Science for Policy Report. European Commission.
  52. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Thousand Oaks, CA: Sage.
  53. Woongjin. (2020). Wonngjin thinkbig AI. Internal report. Seoul: Woongjin co.
  54. Woo, Y. K., & Kim, S. I. (2015). Structural relationship among academic motivation, engagement and achievement: Domain comparison between mathematics and english. The Korean Journal of Educational Methodology Studies, 27(2), 253-273. https://doi.org/10.17927/tkjems.2015.27.2.253
  55. Yang, A. K., & Cho, H. J. (2009). An analysis on the influence of self-regulated learning upon academic achievement. Journal of Educational Research & Implementation, 8(3), 61-82.
  56. Yang, M. H., & Whang, J. K. (2002). The study on conceptualization of self-regulated learning using LISREL. The Society of Educational Psychology, 6(2), 259-290.