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A Longitudinal Study on the Effect of Teacher Characteristics Perceived by Students on Mathematics Academic Achievement: Targeting Middle and High School Students

학생들이 인식한 교사의 특성이 수학 학업성취도에 미치는 영향에 대한 종단연구: 중·고등학교 학생을 대상으로

  • Received : 2021.02.09
  • Accepted : 2021.03.12
  • Published : 2021.03.31

Abstract

Since the characteristics of teachers that affect mathematics academic achievement are constantly changing and affecting mathematics achievement, longitudinal studies that can predict and analyze growth are needed. This study used data from middle and high school students from 2013(first year of middle school) to 2017(second year of high school) of the Seoul Education Longitudibal Study(SELS). By classifying the longitudinal changes in mathematics academic achievement into similar subgroups, the direct influence of teachers' characteristics(professionalism, expectations, academic feedback) perceived by students on the longitudinal changes in mathematics academic achievement was examined. As a result of the study, it was found that the characteristics of mathematics teachers(professional performance, expectation, and academic feedback) in group 1(343 students), which included the top 14.5% of students, did not directly affect longitudinal changes in mathematics academic achievement. Students in the middle 2nd group(745, 32.2%) had academic feedback from the mathematics teacher, and the 2nd group(1225 students) in the lower 53%, which included most of the students, showed that the expectations of the mathematics teacher were the longitudinal mathematics achievement. The change has been shown to have a direct effect. This suggests that support for teaching and learning should also reflect this, as the direct influence of teachers' professionalism, expectations, and academic feedback on longitudinal changes in mathematics academic achievement is different according to the characteristics and dispositions of students.

수학 학업성취도에 영향을 미치는 교사의 특성은 끊임없이 변화하면서 수학 학업성취도에 영향을 미치고 있기 때문에 성장을 예측·분석할 수 있는 종단연구가 필요하다. 본 연구는 서울교육종단연구(SELS)의 중학교 1학년(2013년)부터 고등학교 2학년(2017년)까지의 중·고등학교 학생자료를 사용하여 수학 학업성취도의 종단적 변화양상이 유사한 하위 그룹으로 분류하여 학생들이 인식한 교사의 특성(전문성, 기대감, 학업적 피드백)이 수학 학업성취의 종단적인 변화양상에 미치는 직접적인 영향과 영향력을 살펴보았다. 연구결과 수학 학업성취도가 상위인 1그룹(343명, 14.5%) 수학교사의 특성(전문성과, 기대감, 학업적 피드백)은 수학 학업성취도의 종단적인 변화에 직접적인 영향을 미치지 않았으며, 중위의 3그룹(745명, 32.2%)은 수학교사의 학업적 피드백, 하위 53%의 2그룹(1225명)은 수학교사의 기대감이 수학 학업성취도의 종단적인 변화에 직접적인 영향을 미치는 것으로 나타났다. 이것은 학생들의 특성과 성향에 따라서도 교사의 전문성과, 기대감, 학업적 피드백이 수학 학업성취도의 종단적인 변화에 미치는 직접적인 영향이 다르므로 교수·학습의 지원도 이러한 점을 반영해야함을 시사해준다.

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. Gu, W. H. (2016). An Exploration of Implication by Analysing Qualitative Characteristics of Developmental Process to Teacher Professional Growth. Humanities Society Research, 17(1), 467-504.
  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., Im, H. J., & Kim, N. O. (2012). An Analysis on Class and Teacher Level Variables Affecting Academic Achievement. Journal of Korean Education, 39(2), 157-179.
  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, 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
  7. Kim, Y. S., & Han, S, Y (2020). Longitudinal Study on the Relationship and Effects of Internal and External Factors on Mathematics Academic Achievement -For Middle and High School Students-. Communications of mathematical education, 34(3), 325-354. https://doi.org/10.7468/JKSMEE.2020.34.3.325
  8. 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
  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. 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.
  15. 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.
  16. 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
  17. Lee, H. M. (2001). Theory of Academic Performance Decision, Seoul: Dankook University Press.
  18. 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
  19. 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.
  20. Jung, H. S. (2019). Longitudinal mediation effect of mathematics class factors between goal perception and mathematics academic achievement on middle school students. Mathematics Education, 58(1), 21-39.
  21. Chi, E. L. (2009). Exploring the Factors and Key Aspects of Teachers' Feedback Practice. Asian journal of education. 10(3), 77-102. https://doi.org/10.15753/AJE.2009.10.3.004004
  22. 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.
  23. Han, M. H. (1997). Securing the expertise of the secondary school teacher training curriculum: focusing on the arguments of the curriculum structure. Korean journal of educational research, 35(5), 171-194.
  24. Hong, S. U. (2009). Analysis of large-scale academic achievement evaluation data using growth model, Collection of the 3rd KICE Curriculum Evaluation Policy Forum.
  25. 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.
  26. Bloom, B. S., Hastings, J. T., & Madaus, G. F. (1971). Handbook on the Formative and Summative Evaluation of Student Learning. York: McGraw-Hill Book Co.
  27. 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
  28. 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.
  29. Good, T. L., Cooper, H. M., & Blakey, S. L. (1980). Classroom Interaction as a Function of Teacher Expectations, Student Sex, Time of Year. Journal of Educational Psychology, 72(3), 378-385. https://doi.org/10.1037/0022-0663.72.3.378
  30. Hattie, J. (2003). Teachers make a difference. What is the research evidence?. Paper presented at the Australian Council for Educational Research. Melbourne, Victoria.
  31. 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
  32. Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. NY: The Guilford Press.
  33. Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking: Methods and practices (3rd ed.). New York: Springer.
  34. 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
  35. Mandel, H. P., & Marcus, S. I. (1988). The psychology of underachievement: Differential diagnosis and differential treatment. New York: John Wiley & Sons.
  36. 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.
  37. 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
  38. NCTM. (2000). Principles and Standards for School Mathmatics. Reston. VA: NCTM.
  39. Sanders, W. L. (1998). Valued-Added Assessment. The School Administrator, 55(11), 24-32.
  40. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Researche.r 29(7), 4-14.
  41. 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
  42. Sternberg, R. J., & Wiliams, W. M. (2009). Educational Psychology. 김정섭, 신경숙, 유순화 공역(2013). 스턴버그의 교육심리학. 서울: 시그마프레스.
  43. Weinstein, R. S.(2002). Reaching higher: The power of expectations in schooling. Cambridge, MA: Harvard University Press.
  44. Wright, D. B. (2017). Some Limits Using Random Slope Models to Measure Academic Growth. In Frontiers in Education, 2(58), Frontiers.