$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

수학수업 태도, 분위기, 만족도가 수학 학업성취도에 미치는 영향에 대한 종단연구
A Longitudinal Study on the Influence of Attitude, Mood, and Satisfaction toward Mathematics Class on Mathematics Academic Achievement 원문보기

Journal of the Korean Society of Mathematical Education. Series E: Communications of Mathematical Education, v.34 no.4, 2020년, pp.525 - 544  

김용석 (성균관대학교)

초록
AI-Helper 아이콘AI-Helper

학업성취도에 영향을 미치는 요인은 다양하며, 요인들이 미치는 영향 또한 복합적으로 일어난다. 학업성취도에 영향을 미치는 요인들은 끊임없이 변화하기 때문에 성장을 예측·분석하는 종단연구가 필요하다. 본 연구는 서울교육종단 연구의 2014년도(중학교 2학년)부터 2017년(고등학교 2학년)까지의 종단자료를 활용하여 수학 학업성취도의 종단적인 변화양상이 유사한 그룹으로 나누고 그룹별 수학수업 태도, 분위기, 만족도의 변화양상과 영향을 살펴보았다. 연구결과, 1그룹(1456명, 68.3%)과 2그룹(677명, 31.7%) 학생들의 수학 학업성취도는 수학수업 태도가 직접적인 영향을 미치는 것으로 나타났으며, 수학수업 분위기와 만족도는 직접적인 영향을 미치지 못하는 것으로 나타났다. 또한, 수학수업 태도가 수학 학업성취도에 미치는 영향력은 그룹에 따라서도 다르게 나타났으며, 수학 학업성취도가 높은 2그룹의 학생들은 1그룹의 학생들보다 수학수업 태도, 분위기, 만족도가 높은 것으로 나타났다. 그리고 수학수업 태도와 분위기, 만족도는 중학교 2학년부터 고등학교 2학년기간 동안 지속적으로 변화하는 것으로 나타났으며, 그 변화의 폭은 적은 것으로 나타났다.

Abstract AI-Helper 아이콘AI-Helper

There are many factors that affect academic achievement, and the influences of those factors are also complex. Since the factors that influence mathematics academic achievement are constantly changing and developing, longitudinal studies to predict and analyze the growth of learners are needed. This...

주제어

표/그림 (21)

참고문헌 (36)

  1. Kim, K. H., Lim, E. Y., & Shin, J. A. (2013). Analysis and prediction of students's affective domain based on the results of National Assessment of Educational Achievement, Journal of Educational Evaluation, 26(5), 981-1014. 

  2. Kang, M. J. (2018). Longitudinal Analysis of High School Students' Affective Attitude, Recognition of Teacher's Teaching Ability, Learning Strategy, and Achievement in Mathematics, Doctoral thesis, Iwaki Womans University. 

  3. Ko, 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. 

  4. Kim, S. S., & Ko, M. S. (2007). The Factor of Effect in Growth of Academic Achievement in Adolescent : The Use of Latent Growth Model, Studies on Korean Youth, 18(3), 5-29. 

  5. Kim, S. I. (2006). Learner-centered interdisciplinary reform: contribution to educational psychology. Proceedings of the Fall Conference of the Korean Educational Association. 

  6. 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. 

  7. Kim, Y. B., & Kang, H. S. (2017). Student Achievement Growth among Middle School Students. Journal of Korean Education, 44(1), 33-61. 

  8. 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. 

  9. 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. 

  10. No, G. S. (2014). Well-informed Thesis Statistical analysis. Han Bit Academy. 

  11. 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. 

  12. Shin, J. C., Jung J. S., & Shin T. S. (2008). Causal Relations Between College Student Academic Achievement and Its Factors, The Journal Educational Administraion, 26(1), 287-313. 

  13. Shin, J. H., & Shin, T. S. (2006). The Analysis of Relations between Academic Achievement, Academic Self-efficacy, Perceived Teacher Expectancy, and Home Environment, The Journal of Child Education, 15(1), 5-23. 

  14. Yang, P. (2017). Analysis of the Effects of Educational Media Usage and Learning Process on Middle School Students' Academic Achievement and Learning Effectiveness: Focused on the seniors of middle school in Seoul. Doctoral thesis, Sungshin University. 

  15. Yoo, H. J. (2016). A Study of the Influence That Learner's Prior Proficiency in Spanish and Participation in Class Have on Their Satisfaction in Clsss and Academic Achievement: Focusing on Spanish Major in Universities. Master's thesis, Hankuk University of Foreign Studies. 

  16. Lee, J. H., & Park, S. M. (2011). Current Conditions and Students' Perception on Mathematics Exhibition, School Mathematics, 13(2), 229-243. 

  17. Lim, H. J. (2016). Structural relationships among variables affecting the math in-class attitude of middle school students: Focusing on the student-centered teaching effect at regular and innovation schools. Secondary Education Research, 64(4), 1075-1104. 

  18. Chung, Y. K., Lee, S. Y., Song, J. Y., & Woo, Y. K. (2017). Differential relations of students' perceived instructions to their motivation, classroom attitude, and academic achievement: The moderating role of self-efficacy, Education Method Research, 29(1), 211-235. 

  19. Brookover, W. B., Schweitzer, J. H., Schneider, J. M., Beady, C. H., Flood, P. K., & Wisenbaker, J. M. (1978). Elementary school social climate and school achievement. American educational research journal, 15(2), 301-318. 

  20. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage Focus Edition, 154, 136-162. 

  21. Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 723-733. 

  22. Corcoran, M. (2000). Mobility, persistence, and the intergenerational determinants of children's success, Focus, 21(2), 16-20. 

  23. 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. 

  24. 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. 

  25. 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. 

  26. Hackman, M. Z., & Walker, K. B. (2009). Instructional communication in the televised classroom: The effects of system design and teacher immediacy on student learnimng and satisfaction, Communication Education, 39(3), 196-206. 

  27. Jung, T., & Mickrama, K. A. S. (2007). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302-317. 

  28. Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. NY: The Guilford Press. 

  29. Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking: Methods and practices (3rd ed.). New York: Springer. 

  30. 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. 

  31. 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. 

  32. 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. 

  33. Muthen, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469. 

  34. 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. 

  35. Wright, D. B. (2017). Some Limits Using Random Slope Models to Measure Academic Growth. In Frontiers in Education. Frontiers, 2(58). 

  36. 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. 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로