Education of Primary School Mathematics (한국수학교육학회지시리즈C:초등수학교육)

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

DOI QR Code

A Longitudinal Analysis of the Influence of Teachers' Achievement Pressure and Enthusiasm Perceived by Students on Academic Achievement in Mathematics: For Elementary and Middle School Students

학생들이 인지하는 교사의 성취압력과 열의가 수학 학업성취도에 미치는 영향력에 대한 종단적 분석: 초·중학생들을 대상으로

  • Received : 2021.06.25
  • Accepted : 2021.07.26
  • Published : 2021.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Achievement pressure and enthusiasm affecting mathematics academic achievement are constantly changing and affecting academic achievement. Therefore, a longitudinal study is needed to examine the influence of the change patterns of teachers' achievement pressure and enthusiasm on the change patterns of academic achievement. This study utilized student data from the 5th grade of elementary school (2013 year) to the third grade of middle school (2017 year) of the Korean Education Longitudinal Study 2013. The longitudinal change patterns of mathematics academic achievement were classified into similar subgroups and the influence of the longitudinal change patterns of the achievement pressure and enthusiasm of each group on the longitudinal change pattern of mathematics academic achievement and the path were compared and analyzed. As a result of the analysis, in all four subgroups with similar longitudinal changes in mathematics academic achievement, the teacher's achievement pressure showed little change from the fifth grade, while the teacher's enthusiasm continued to decline from the fifth grade. In addition, the influence of teachers' achievement pressure and enthusiasm perceived by students in each group on mathematics academic achievement was different. This suggests that in order to improve mathematics academic achievement, it is necessary to support teaching and learning reflecting the characteristics and dispositions of students.

수학 학업성취도에 영향을 미치고 있는 교사의 성취압력과 열의는 끊임없이 변화하면서 수학 학업성취도 영향을 미치므로 그 변화양상이 학업성취도의 변화양상에 미치는 영향력을 살펴볼 수 있는 종단연구가 필요하다. 본 연구는 한국교육종단연구2013의 초등학교 5학년(2013년)부터 중학교 3학년(2017년)까지의 학생 데이터를 활용하여 수학 학업성취도에 대한 종단적인 변화양상이 유사한 하위 그룹으로 분류하여 그룹별 교사의 성취압력과 열의에 대한 종단적인 변화양상이 수학 학업성취도의 종단적인 변화양상에 미치는 영향력과 그 경로를 비교·분석하였다. 분석결과 수학 학업성취도의 종단적인 변화가 유사한 4개의 하위그룹 모두 교사의 성취압력은 초등학교 5학년부터 변화가 적은 반면에 교사의 열의는 초등학교 5학년부터 지속적으로 떨어지는 것으로 나타났다. 또한, 그룹별 학생들이 인지하는 교사의 성취압력과 열의가 수학 학업성취도에 미치는 영향력은 다르게 나타났다. 이것은 수학 학업성취도의 향상을 위해서는 학생들의 특성과 성향을 반영한 교수 학습의 지원이 필요하다는 것을 시사한다.

Keywords

References

  1. Go, Y. J. (2018). A study on analysis of actual state of mathmatics renouncers and treatment at the renouncer's level, Master's thesis, Ulsan University Graduate School of Education.
  2. Kim, S. S. (2007). A study on the effect of school climates on student achievement in Korean middle school: in terms of improving achievement and closing a gap. The Journal of Korean Education, 34(2), 27-49.
  3. Kim, Y. B., Im, H. J., & Kim, N. O. (2012). An Analysis on Class- and Teacher-Level Variables Affecting Academic Achievement. Journal of Korean Education, 34(2), 27-49.
  4. 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.
  5. Kim, Y. S. (2021). A Longitudinal Study on the Influence of Learning Effort, Attitude, and Achievement Goal on Mathematics Academic Achievement: For elementary and secondary school students. Education of Primary School Mathematics, 24(1), 1-20. https://doi.org/10.7468/JKSMEC.2021.24.1.1
  6. Kim, J. Y., Jang, J. H., & Park, I. W. (2017). A Study on the Effects of the Teachers Characteristics Recognized by Students on Student's Attitude, Self-directed Learning and Academic Achievement. Secondary Education Research, 65(4), 731-758. https://doi.org/10.25152/ser.2017.65.4.731
  7. Kim, J. H., Hong, S. H., & Kim, M. G. (2009). Writing a thesis with structural equations. Communication Books.
  8. 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
  9. Kim, J. H., & Seo, S. B. (2020). A Study on the Longitudinal Effect of Middle School Students' Self-Recognition, Academic Self-Efficacy, School Adjustment, and Academic Achievement. Journal of Learner-Centered Curriculum and Instruction, 20(10), 365-383.
  10. Kim, H. W., & A, S. K. (2021). The relationship among academic self concept, parent's achievement pressure and school's achievement pressure. Journal of Learner-Centered Curriculum and Instruction, 11(3), 1-18.
  11. 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
  12. No, G. S. (2014). Well-informed Thesis Statistical analysis. Han Bit Academy.
  13. Ryu, B. R., & Kim, S. S. (2006). Educational Gap: An analysis of the influence of family background and schooling. Seoul: Korea Educational Development Institute.
  14. Park, K. H. (2020). Korean Education Longitudinal Study 2013 (VIII): Survey Overview Report. Korea Educational Development Institute.
  15. 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.
  16. Park, H. U., Choi, B. H., & Shin, J. H. (2017). A Longitudinal Relationship among Disruptive Classroom Behavior, Teacher Enthusiasm, and Coercive Discipline in Middle School Students using Autoregressive Cross-lagged Model. Asian journal of education, 18(4), 563-588. https://doi.org/10.15753/aje.2017.12.18.4.563
  17. Baem B. R. (2016). Mplus 7.0 structural equation modeling. Cheongnam publishing.
  18. Baek, B. D. (2013). Longitudinal effects of high school students' perceived academic press from teachers on academic achievement: focusing on the interaction with socio-economic status. Journal of Korean Education, 40(2), 31-55.
  19. Soo, Y. H. (2011). Analysis of the structural relations between Learners' perception on instruction, self-directed Learning, Learning flow, and academic achievement. The Journal of Child Education, 20(2), 19-32.
  20. Son, Y. H. (2016). The Longitudinal Effects of student's Private Tutoring Time and Self-Studying Time on Longitudinal Change of Academic Achievement. Master's thesis, Seoul National University.
  21. 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.
  22. Song, J. S. (2015). SPSS/AMOS statistical analysis method required for thesis writing. 21st century history.
  23. Lee, H. S., & Chung, J. Y. (2001). An Analysis of the Influence of Teachers' Traits on Student Achievement - Focusing on Teachers' Efforts to Enhance Professionality in TIMSS 2007. The Journal of Korean Teacher Education, 28(1), 243-266. https://doi.org/10.24211/tjkte.2011.28.1.243
  24. Cheong, M. J., Kim, H. K., & Moon, Y. H. (2015). The relationship between Teaching Methods accepted by learners and Academic Achievement Factors on Academic Achievement. Korean journal of youth studies, 27(2), 129-150.
  25. Chi, E. L., Yang, M. H., & Cheong, Y. S. (2011). Effects of teachers' activities for instruction and evaluation on students' self-regulated Learning and academic achievement. The Journal of Elementary Education, 24(4), 165-184.
  26. Choi, D. H. (2012). The effect of school achievement pressure on academic achievement: Focusing on the discriminatory effect according to socioeconomic background. Master's thesis, Korea University.
  27. Hyun, J. C., & Cho, M. H. (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
  28. Korea Educational Development Institute. (2020). Data collection of the 14th Korean Educational Longitudinal Research Conference. Korea Educational Development Institute.
  29. Hong, S. U. (2009). Analysis of large-scale academic achievement evaluation data using growth model. Collection of the 3rd KICE Curriculum Evaluation Policy Forum.
  30. Bettencourt, E. M. (1980). Effects of training teachers in enthusiasm on student achievement and attitudes.
  31. Brigham, F. J., Scruggs, T. E., & Mastropieri, M. A. (1992). Teacher enthusiasm in learning disabilities classrooms: effects on learning and behavior. Learning Disabilities Research & Practice, 7(2), 68-73.
  32. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long(Eds), Testing structural equation models(pp. 136-162). Newbury Park, ca: SAGE.
  33. Celeux, G., & Soromenho, G.(1996) An entropy criterion for assessing the number of cluster in a mixture model. Journal of Classification, 13, 195-212. https://doi.org/10.1007/BF01246098
  34. 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
  35. Duncan, T. E., & Duncan, S. C. (2004). An introduction to latent growth curve modeling. Behavior Therapy, 35(2), 333-363. https://doi.org/10.1016/S0005-7894(04)80042-X
  36. Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling :concepts, issues, and applications. Mahwah, N.J. : Lawrence Erlbaum Associates.
  37. Hattie, J. (2003). Teachers make a difference. What is the research evidence?. Paper presented at the Australian Council for Educational Research. Melbourne, Victoria.
  38. 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
  39. Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. NY: The Guilford Press.
  40. Kline, R. B. (2010). Principles and Practice of Structural Equation Modeling. (Third ed.). NY: The Guilford Press.
  41. Kunter, M., Frenzel, A., Nagy, G., Baumert, J., & Pekrun, R. (2011). Teacher enthusiasm: Dimensionality and context specificity. Contemporary Educational Psychology, 36, 289-301. https://doi.org/10.1016/j.cedpsych.2011.07.001
  42. Long, J. F., & Hoy, A. W. (2006). Interested instructors: A composite portrait of individual differences and effectiveness. Teaching and Teacher Education, 22(3), 303-314. https://doi.org/10.1016/j.tate.2005.11.001
  43. 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
  44. MacCallum, R. C., & Kim, C. (2000). Modeling multi variate change. InT. D. Little, K. U. Schnabel, & U. Lindenberg(Eds.), Modeling Longitudinaland Multiple-Group Data: Practical Issues, Applied Approaches, and Specific Examples. Mahwah, NJ: Lawrence Erlbaum.
  45. Martin, A. J., Linfoot, K., & Stephenson, J. (1999). How teachers respond to concerns about misbehavior in their classroom. Psychology in the Schools, 36(4), 347-358. https://doi.org/10.1002/(SICI)1520-6807(199907)36:4<347::AID-PITS7>3.0.CO;2-G
  46. McLachlan, G., & Peel, D.(2000). Finite mixture models. New York: Wiley.
  47. Mullis, I. V. S., Martin, M. O., Ruddock, G. J., O'Sullivin, C. Y., Arora, A., & Erberber, E. (2005). TIMSS 2007 Assessment Framework. TIMSS & PIRLS International Study Center, Lynch School of Education. Boston College.
  48. Murphy, J. F., Weil, M., Hallinger, P. & Mitman, A. (1982). Academic Press: Translating High Expectations into School Policies and Classroom Practices. Educational Leadership, 40(3), 22-26.
  49. 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.
  50. Muthen, B., & Muthen, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and experimental research, 24(6), 882-891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x
  51. NCTM. (2000). Principles and Standards for School Mathmatics. Reston. VA: NCTM.
  52. Okada, H., Hirano, D., & Taniguchi, T. (2020). Negative symptoms in schizophrenia: modeling the role of experience factor and expression factor. Asian Journal of Psychiatry, 53, 102182. https://doi.org/10.1016/j.ajp.2020.102182
  53. So, H. J., & Kim, B. (2009). Learning about problem based learning: Student teachers integrating technology, pedagogy and content knowledge. Australasia Journal of Educational Technology, 25(1), 101-116. https://doi.org/10.17232/KSET.25.4.101
  54. Sternberg, R. J., & Wiliams, W. M. (2013). Educational Psychology (김정섭, 신경숙, 유순화 역). 스턴버그의 교육심리학. 서울: 시그마프레스. (원저 2009 출간)
  55. Wang, M.-C., Deng, Q., Bi, X., Ye, H., & Yang, W. (2017). Performance of the entropy as an index of classification accuracy in latent profile analysis: A Monte Carlo simulation study. Acta Psychologica Sinica, 49(11), 1473-1482. https://doi.org/10.3724/SP.J.1041.2017.01473