본문 바로가기
KERD
논문KCI 등재

Learning motivation of groups classified based on the longitudinal change trajectory of mathematics academic achievement: For South Korean students

김용석
한국수학교육학회|2024.03|Mathematics Education Research and Practice|제27권 제1호|pp. 129-150|DOI https://doi.org/10.7468/jksmed.2024.27.1.129

2

조회

0

다운로드

1

인용

0

참고문헌

키워드

self-determination theorylearning motivationlongitudinal datagrowth mixture modellinglatent growth modelmathematics academic achievement

초록

This study utilized South Korean elementary and middle school student data to examine the longitudinal change trajectories of learning motivation types according to the longitudinal change trajectories of mathematics academic achievement. Growth mixture modeling, latent growth model, and multiple indicator latent growth model were used to examine various change trajectories for longitudinal data. As a result of the analysis, it was classified into 4 subgroups with similar longitudinal change trajectories of mathematics academic achievement, and the characteristics of the mathematics subject, which emphasize systematicity, appeared. Furthermore, higher mathematics academic achievement was associated with higher self-determination and higher academic motivation. And as the grade level increases, amotivation increases and self-determination decreases. This study suggests that teaching and learning support using this is necessary because the level of learning motivation according to self-determination is different depending on the level of mathematics academic achievement reflecting the characteristics of the student.

Abstract

This study utilized South Korean elementary and middle school student data to examine the longitudinal change trajectories of learning motivation types according to the longitudinal change trajectories of mathematics academic achievement. Growth mixture modeling, latent growth model, and multiple indicator latent growth model were used to examine various change trajectories for longitudinal data. As a result of the analysis, it was classified into 4 subgroups with similar longitudinal change trajectories of mathematics academic achievement, and the characteristics of the mathematics subject, which emphasize systematicity, appeared. Furthermore, higher mathematics academic achievement was associated with higher self-determination and higher academic motivation. And as the grade level increases, amotivation increases and self-determination decreases. This study suggests that teaching and learning support using this is necessary because the level of learning motivation according to self-determination is different depending on the level of mathematics academic achievement reflecting the characteristics of the student.

참고문헌 (37)

  1. Bishop, J., "Modeling latent growth with multiple indicators : A comparison of three approaches", Psychological Methods, 20(1), pp.43~62 (2015)[DOI]
  2. Bollen, K. A., "Latent curve models: A structural equation perspective" (2006), John Wiley & Sons
  3. Chan, D. K. C., "In-lecture learning motivation predicts students’ motivation, intention, and behaviour for after-lecture learning : Examining the trans-contextual model across universities from UK, China, and Pakistan", Motivation and Emotion, 39(6), pp.908~925 (2015)[DOI]
  4. Curran, P. J., "The robustness of test statistics to nonnormality and specification error in confirmatory factory analysis", Psychological Methods, 1(1), pp.16~29 (1996)[DOI]
  5. Deci, E. L., "The general causality orientations scale : Self-determination in personality", Journal of Research in Personality, 19(2), pp.109~134 (1985)[DOI]
  6. Deci, E. L., "The ‘What’ and ‘Why’ of goal pursuits : Human needs and the self-determination of behavior", Psychological Inquiry, 11(4), pp.227~229 (2000)[DOI]
  7. Deci, E. L., "Self-determination theory : A macrotheory of human motivation, development, and health", Canadian Psychology, 2008, pp.49(3), 182~185
  8. Diseth, Å., "A comparison of intrinsic and extrinsic motivation among lower and upper secondary school students", Educational Psychology, 40(8), pp.961~980 (2020)[DOI]
  9. Duke, N. K., "Putting PjBL to the test : The impact of project-based learning on second graders’ social studies and literacy learning and motivation in low-SES school settings", American Educational Research Journal, 58(1), pp.160~200 (2021)[DOI]
  10. Duncan, T. E., "An introduction to latent growth curve modeling", Behavior Therapy, 35(2), pp.333~363 (2004)[DOI]
  11. Duncan, T. E., "An introduction to latent variable growth curve modeling: Concepts, issues, and applications" (2013), Routledge
  12. Filgona, J., "Motivation in learning", Asian Journal of Education and Social Studies, 10(4), pp.16~37 (2020)[DOI]
  13. Geary, D. C., "Cognitive predictors of achievement growth in mathematics : A 5-year longitudinal study", Developmental Psychology, 47(6), pp.1539~1552 (2011)[DOI]
  14. Guay, F., "Applying self-determination theory to education : Regulations types, psychological needs, and autonomy supporting behaviors", Canadian Journal of School Psychology, 37(1), pp.75~92 (2022)[DOI]
  15. Development of items in the 5th year of the Gyeonggi education longitudinal study, "Kang, H. S.", Gyeonggi Education Research Institute, ~ (2016)
  16. Kim, A. Y., "Classification of Motivation on the Continuum of Self-Determination", Journal of Education Psychology, 15(4), pp.97~119 (2001)
  17. Research on the achievement score for the academic achievement test for the 7th year Seoul National University of Education, "Kim, S. H.", Seoul Institute for Education Policy, ~ (2016)
  18. Kim, Y. S., "A longitudinal study on the effect of learner's internal and external factors on mathematics academic achievement: For middle and high school students", Doctor, pp.2020
  19. Kline, R. B., "Principles and practice of structural equation modeling" (2016), Guilford Press
  20. Lee, M. B., "The effect of self-determination motivation and self-regulated learning on academic achievement", Master, pp.2003
  21. Leech, N. L., "IBM SPSS for intermediate statistics: Use and interpretation" (2014), Routledge
  22. Little, T. D., "Longitudinal structural equation modeling" (2013), Guilford Press
  23. Mok, M. M., "Growth trajectories of mathematics achievement : Longitudinal tracking of student academic progress", British Journal of Educational Psychology, 85(2), pp.154~171 (2015)[DOI]
  24. Newsom, J. T., "Longitudinal structural equation modeling: A comprehensive introduction" (2015), Routledge
  25. Perez Mejias, P., "A longitudinal study of the gender gap in mathematics achievement : evidence from Chile", Educational Studies in Mathematics, 107(3), pp.583~605 (2021)[DOI]
  26. Pintrich, P. R., "Motivation in education: Theory, research, and applications" (2002), Prentice Hall
  27. Prislin, R., "Minority influence : An agenda for study of social change", Frontiers in Psychology, 13, pp.911654~ (2022)[DOI]
  28. Ryan, R, M., "Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being", American Psychologist, 55(1), pp.68~78 (2000)[DOI]
  29. Ryan, R. M., "Self-determination theory: Basic psychological needs in motivation, development, and wellness" (2017), Guilford Publications
  30. Ryan, R. M., "Intrinsic and extrinsic motivation from a self-determination theory perspective : Definitions, theory, practices, and future directions", Contemporary Educational Psychology, 61, pp.101860~ (2020)[DOI]
  31. Shin, J., "Student and school factors affecting mathematics achievement : International comparisons between Korea, Japan and the USA", School Psychology International, 30(5), pp.520~537 (2009)[DOI]
  32. Gyeonggi education longitudinal research rese math academic achievement, learning motivation, self-determination theory, growth mixture modelling, latent growth model arch design and item development research, "Sung, K. S.", Korea Education Research Network, ~ (2012)
  33. Tremblay-Wragg, É., "The use of diversified teaching strategies by four university teachers : What contribution to their students’ learning motivation", Teaching in Higher Education, 26(1), pp.97~114 (2021)[DOI]
  34. Wang, M. -C., "Performance of the entropy as an index of classification accuracy in latent profile analysis : A Monte Carlo simulation study", Acta Psychologica Sinica, 49(11), pp.1473~1482 (2017)[DOI]
  35. Wang, J., "Structural equation modeling: Applications using Mplus" (2019), John Wiley & Sons
  36. Zaccone, M. C., "The effects of intrinsic and extrinsic motivation on students learning effectiveness. Exploring the moderating role of gender", International Journal of Educational Management, 33(6), pp.1381~1394 (2019)[DOI]
  37. Zheng, Y., "Does adding video and subtitles to an audio lesson facilitate its comprehension", Learning and Instruction, 77, pp.101542~ (2022)[DOI]

이 간행물의 다른 논문 (7건)

How do one expert mathematics teacher in China implement deep teaching in problem-solving and problem-posing classroom: A case studyYanhui Xupp. 1-24U.S. and Korean teacher candidates' approaches to mathematical modeling on a social justice issueEunhye Flavin, 황성환pp. 25-47A comparative study of the revised 2022 Korea mathematics curriculum and the international baccalaureate diploma program mathematics: Applications and interpretation standard level - focusing on high school statistics area이수빈, 권오남, 조아라pp. 49-73Investigating the substance and acceptability of empirical arguments: The case of maximum-minimum theorem and intermediate value theorem in Korean textbooks김한길pp. 75-92Comparison perceptions of secondary mathematics teachers between Korea and Indonesia in covid-19 era손태권, 이광호, Ari Widodopp. 93-109
원문 보기