Education of Primary School Mathematics (한국수학교육학회지시리즈C:초등수학교육)

Korean Society of Mathematical Education (한국수학교육학회)
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A Longitudinal Study on the Influence of Learning Effort, Attitude, and Achievement Goal on Mathematics Academic Achievement : For elementary and secondary school students

학습노력, 태도 및 성취목표가 수학 학업성취도에 미치는 직·간접적인 영향에 대한 종단연구: 초·중학생을 대상으로

  • Received : 2020.12.11
  • Accepted : 2021.01.25
  • Published : 2021.01.31
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Abstract

Factors influencing mathematics academic achievement are constantly changing and have direct and indirect effects on mathematics achievement, so longitudinal studies that can predict and analyze their growth are needed. This study uses longitudinal data on students from 2011 (5th grade of elementary school) to 2015 (2nd grade of middle school) of the Seoul Education Longitudinal Study, and divides them into groups with similar longitudinal changes in mathematics academic achievement. The direct and indirect effects of learning attitudes and achievement goals were examined. As a result of the study, it was found that learning effort and learning attitude had a direct effect on mathematics achievement in 1 group (2277 students, 67.7%), and learning attitude had a direct effect on mathematics achievement in 3 groups (958 students, 28.5%). And it was found that learning effort h ad an indirect effect. In addition, it was found that both learning attitudes, learning efforts, and achievement goals had no effect on the academic achievement of mathematics in the second group (127 students, 3.8%).

수학 학업성취도에 영향을 미치는 요인들은 끊임없이 변화하면서 수학 학업성취도에 직·간접적인 영향을 미치고 있기 때문에 그 성장을 예측하고 분석할 수 있는 종단연구가 필요하다. 본 연구는 서울교육종단연구의 2011년도(초등학교 5학년)부터 2014년(중학교 2학년)까지 학생들에 대한 종단자료를 활용하여 수학 학업성취도의 종단적인 변화양상이 유사한 그룹으로 분류하여 그룹별 학습노력과 학습태도, 성취목표의 직·간접적인 영향을 살펴보았다. 연구결과 1그룹(2277명, 67.7%)의 수학 학업성취도에는 학습노력과 학습태도가 직접적인 영향을 미치는 것으로 나타났으며, 3그룹(958명, 28.5%)의 수학 학업성취도에는 학습태도가 직접적인 영향, 학습노력은 학습태도를 매개로 하여 간접적인 영향을 미치는 것으로 나타났다. 또한 2그룹(127명, 3.8%)의 수학 학업성취도에는 학습태도와 학습노력, 성취목표 모두 영향을 미치지 못하는 것으로 나타났다.

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References

  1. Kwak, S. R. (2009). The effects of school factors on academic achievement. Korean journal of educational research, 47(3), 93-120.
  2. Kim, S. S., Kim, Y. B., Kang, S. C., Kim, H. C., & Shin, J. H. (2007). Korean Education Longitudian Study 2005(III): Descriptive reoprt. Seoul: KEDI.
  3. Kim, S. H., Kang, D. H., Moon, S. M., Yoon, W. S., & Park, S. H. (2016). Research on the achievement score for the academic achievement test for the 7th year Seoul National University of Education, Seoul Institute for Education Policy, Institute of Education and Research Information.
  4. Kim, Y. B., & Kang, H. S. (2017). Student Achievement Growth among Middle School Students. Journal of Korean Education, 44(1), 33-61.
  5. Kim, Y. S. (2020). A longitudinal study on the effect of learner's internal and external factors on mathematics academic achievement: For middle and high school students. Doctoral thesis, Sungkyunkwan University.
  6. Kim, J. H. (1998). A Study on the Teacher Class Evaluation of High School Students. Doctoral thesis, Chungnam University.
  7. Kim, J. H. (2001). Relationships of Self-esteem, Self-efficacy, Learning Attitude and Subject Fondness in Academic Achievement at High School Level: A Regression Analysis. Korean Journal of Education Research, 39(4), 349-366.
  8. Kim, J. Y., Park, I. Y., & Si, K. J. (2014). Factors affecting within-school achievement gap and school classification based on the NAEA scale score change. Journal of Educational Evaluation, 27, 1057-1082.
  9. Kim, J. W., Yang., J. Y., Lee, C. A., & Hong, S. H. (2019). Changes in Retiree's Depression after Retirement: Applying Growth Mixture Model, survey research, 20(1), 45-72. https://doi.org/10.20997/SR.20.1.3
  10. Kim, H. M., Kim, Y. S., & Han, S. Y. (2018). A Longitudinal Analysis on the Relationships Among Mathematics Academic Achievement, Affective Factors, and Shadow Education Participation, School Mathematics, 20(2), 287-306. https://doi.org/10.29275/sm.2018.06.20.2.287
  11. No, G. S. (2014). Well-informed Thesis Statistical analysis. Han Bit Academy.
  12. Park, S. H., & Yoon, W. S. (2018). Seoul Education Longitudibal Study 8th User Manual. Seoul Metropolitan Office of Education Education Research Information Service Education Policy Research Institute.
  13. Park, C. R. (2003). A study on the descent of the function-chapter in the middle school. Master's thesis, Mokpo University Graduate School of Education.
  14. 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.
  15. So, Y. H. (2008). A path analysis on the relationship of perceived social support, academic self-efficacy, achievement motivation, and academic achievement by a school grade. The Journal of Child Education, 17(2), 49-64.
  16. Song, M. Y., Kim, S. S., Yi, H, S., & Kim J, Y. (2011). Investigation on Contextual Variables Affecting Academic Achievement. Journal of Educational Evaluation, 24(2), 261-289.
  17. Shin, H. S., Ryu, J. H., & An, J. E. (2010). Differences between middle school students' learning skills and academic achievement according to achievement goal orientation profile. Education Research, 48(2), 45-66.
  18. Yeo, T. H., Lim, H. J., & Hwang, M. H. (2017). The Relationship between Self-Control and Academic Achievement: The Mediating Roles of Learned Helplessness and Learning Strategies, Education Culture Research, 23(1), 315-341. https://doi.org/10.24159/joec.2017.23.1.315
  19. Yoo, H. H. (2005). Analysis of predictive power of related variables on academic achievement. Doctoral thesis, Hongik University.
  20. Lee, S. J. (2012). The Relationship of Academic Self-efficacy, Task value, Achievement goal, Academic efforts and Cognitive strategies to English academic achievement between Middle and High school Students in Korea. The Journal of Educational Research, 10(4), 103-124.
  21. Lee, J. S. (2012). The influence of self-regulated learning, achievement-goal and academic volitional control on the academic achievement of elementary school students. Master's thesis, Hanyang University Graduate School of Education.
  22. Lee, T. S. (2010). The Relationship between learning attitude and school life adjustment by school type. Journal of Learner Centered Curriculum and Instruction, 10(3). 373-388.
  23. Chung, Y. K. (2020) Structural Relationship among Future Time Perspective, Motivation, Effort and Academic Achievement of Middle School Students. Educational research method, 32(3), 383-404.
  24. Cho, H. C. (2011). The effects of academic motivation, self-determination, goal orientation, self-perception, implicit theory of intelligence and self-regulated learning strategies on learning attitude, behavior and outcomes. The Korean Journal of Educational Psychology, 25(1), 33-60.
  25. Choi, H. & Cho, M. (2014). Identifying latent classes in adolescent's self-determination motivation and testing determinants of classes, The korean journal of school psychology 11(1), 253-274. https://doi.org/10.16983/kjsp.2014.11.1.253
  26. Hong, S. U. (2009). Analysis of large-scale academic achievement evaluation data using growth model, Collection of the 3rd KICE Curriculum Evaluation Policy Forum.
  27. Arbona, C. (2000). The development of academic achievement in school-aged children: Precursors to career development. In S. D. Brown & R. W. Lent (Eds.), Handbook of counseling psychology (3rd ed., pp. 270-309). New York: John Wiley and Sons.
  28. Bloom, B. S. (1976). Human characteristic and school learning. N.Y: McGraw-Hill.
  29. Bruyn, E. H., Dekovic, M., & Meijnen, G. W. (2003). Parenting, goal orientations, classroom behavior and school success in early adolescence. Applied Development Psychology, 24(4), 393-412. https://doi.org/10.1016/S0193-3973(03)00074-1
  30. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factory analysis. Psychological Methods, 1(1), 16-29. https://doi.org/10.1037/1082-989X.1.1.16
  31. DeRoon-Cassini, T. A., Mancini, A. D., Rusch, M. D., & Bonanno, G. A. (2010). Psychopathology and Resilience Following Traumatic Injury: A Latent Growth Mixture Model Analysis. Rehabilitation Psychology, 1(55), 1-11.
  32. Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256-273. https://doi.org/10.1037/0033-295X.95.2.256
  33. Elliot, A. J. & McGregor, H. A. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519. https://doi.org/10.1037/0022-3514.80.3.501
  34. Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 1(1), 8.
  35. Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. https://doi.org/10.1080/10705519909540118
  36. Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. NY: The Guilford Press.
  37. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130
  38. Mandel, H. P., & Marcus, S. I. (1988). The psychology of underachievement: Differential diagnosis and differential treatment. New York: John Wiley & Sons.
  39. Meece, J. L., Blumenfeld, P. C., & Hoyle, R. H. (1988). Students' goal orientations and cognitive engagement in classroom activities. Journalof Educational Psychology, 80, 514-523. https://doi.org/10.1037/0022-0663.80.4.514
  40. Muthen, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469. https://doi.org/10.1111/j.0006-341X.1999.00463.x
  41. Muthen, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences. 346-368. Newbury Park, CA: Sage Publications.
  42. Muthen, B. O., & Asparouhov, T. (2009). Growth mixture modeling: Analysis with non-Gaussian random effects. In Fitzmaurice, G., Davidian, M., Verbeke, G., & Molenberghs, G.(eds.), Longitudinal Data Analysis, pp.143-165. Boca Raton: Chapman & Hall/CRC Press.
  43. Natale, K., Aunola, K., & Nurmi, J. E. (2009). Children's school performance and their parents' causal attributions to ability and effort: A logitudinal study. Journal of Applied Developmental Psychology, 30, 14-22. https://doi.org/10.1016/j.appdev.2008.10.002
  44. NCTM. (2000). Principles and Standards for School Mathmatics. Reston. VA: NCTM.
  45. Sternberg, R. J., & Williams, W. M. (2010). Educational psychology(2nd ed.). NJ: Pearson/Merrill.
  46. Wickrama, K. K., Lee, T. K., O'Neal, C. W., & Lorenz, F. O. (2016). Higher-order growth curves and mixture modeling with Mplus: A practical guide. Routledge.
  47. Wright, D. B. (2017). Some Limits Using Random Slope Models to Measure Academic Growth. In Frontiers in Education. Frontiers, 2(58).
  48. Ye, F., & Daniel, L. (2017). The Impact of Inappropriate Modeling of Cross-Classified Data Structures on Random-Slope Models. Journal of Modern Applied Statistical Methods, 16(2), 25.