최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Journal of the Korean Society of Mathematical Education. Series A. The Mathematical Education, v.63 no.1, 2024년, pp.19 - 33
김래영 (서울대학교) , 한수연 (한국교육과정평가원)
This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics i...
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
Chang, O. (2013). A study on the effect of the private in-advance learning in mathematics among humanities-orientedtrack high school students [Doctoral dissertation, Dankook University].
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, 22(7), 129-150.
Choe, S. H., Park, S., & Hwang, H. J. (2014). Analysis of the current situation of affective characteristics of Korean students based on the results of PISA and TIMSS. Journal of the Korean School Mathematics Society, 17(1), 23-43.
Chung, H., Won, J., & Park, S. (2018). Classifying the academic achievements and core competencies of adolescents and testing the effects of variable factors. Studies on Korean Youth, 29(2), 185-215. http://doi.org/10.14816/sky.2018.29.2.185
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. The Korean Journal of Educational Methodology Studies, 29(1), 211- 235. http://doi.org/10.17927/tkjems.2017.29.1.211
Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 621-638. https://doi.org/10.1080/10705511.2017.1402334
Hertzog, C., von Oertzen, T., Ghisletta, P., & Lindenberger, U. (2008). Evaluating the power of latent growth curve models to detect individual differences in change. Structural Equation Modeling: A Multidisciplinary Journal, 15(4), 541-563. https://doi.org/10.1080/10705510802338983
Jang, J., & Ko, Y. (2020). Perception and characteristics of teachers and students on teaching methods: A latent profile analysis. The Korean Journal of Educational Methodology Studies, 32(4), 575-605.
Jang, J., & Park, I. (2019). Analysis of the actual status and effect of mathematics prerequisite learning of elementary, middle, and high school students in Gyeonggi-do. The Korean Journal of Educational Methodology Studies, 31(1), 45-66. http://doi.org/10.17927/tkjems.2019.31.1.45
Ju, Y. J., Lee, C. H., & Kim, S. H. (2011). A comparison study between male and female students on academic selfefficacy, interest, external motivation, and mathematics achievement of high school students. Journal of Research in Curriculum & Instruction, 15(4), 1021-1043. http://doi.org/10.24231/rici.2011.15.4.1021
Jung, H. S., & Song, H. N. (2020). Detecting types for the influence of mathematics interest and mathematical perception on mathematics achievement in middle school students: Using REBUS-PLS. School Mathematics, 22(4), 853-868. https://doi.org/10.29275/sm.2020.12.22.4.853
Kang, M. (2018). Longitudinal analysis of high school students' affective attitude, recognition of teacher's teaching ability, learning strategy, and achievement in mathematics [Doctoral dissertation, Ewha Womans University].
Kang, T., & Song, M. (2012). An exploratory study on IRT vertical scaling for grade 6 through grade 9 educational achievement tests. Journal of Educational Evaluation, 25(2), 287-315.
Kim, H. M., Kim, Y., & Han, S. (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
Kim, K., Kim, S., Kim, M., & Kim. S. (2009). Comparative analysis of curriculum and achievement characteristics between Korea and high performing countries in PISA & TIMSS (RRE 2009-7-2). Korea Institute for Curriculum and Evaluation.
Kim, K., Kim, S., & Park, H. (2010). A Comparison of multi-level models for mathematics performance across Korea, Singapore, Japan and Hong Kong. The Journal of Curriculum and Evaluation, 13(2), 219-238.
Kim, S., & Koh, M. (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.
Kim, S., & Shin, C. (2011). Academic high school students: the pre-study effect analysis. The Journal of Yeolin Education, 19(4), 87-108.
Kim, S. J., Kim, K, H., & Park, J. H. (2014). The effect of mathematics achievement on changes in mathematics interest and values for middle school students. Journal of Research in Curriculum and Instruction, 18(3), 683-701. http://doi.org/10.24231/rici.2014.18.3.683
Kim, Y. B., & Kim, N. O. (2015). Exploration of student and school factors influencing on academic achievement. Korean Journal of Educational Research, 53(3), 31-60.
Koller, O., Baumert, J., & Schnabel, K. (2001). Does interest matter? The relationship between academic interest and achievement in mathematics. Journal for Research in Mathematics Education, 32(5), 448-470. https://doi.org/10.2307/749801
Lee, C. H., & Kim, S. (2010). Analysis of affective factors on mathematics learning according to the results of PISA 2003. School Mathematics, 12(2), 219-237.
Lee, M., & Kil, Y. (1998). Differences of affective variables related to mathematics learning by the grades and achievement groups. The Mathematical Education, 37(2), 147-158.
Lee, S., & Lee, S. S. (2023). Examining the influence of learner-centered and teacher-centered instruction on middle school students' subject interest by achievement levels. The Korean Journal of Educational Methodology Studies, 35(1), 129-154.
Li, F., Barrera, M., Hops, H., & Fisher, K. J. (2002). The longitudinal influence of peers on the development of alcohol use in late adolescence: A growth mixture analysis. Journal of Behavioral Medicine, 25(3), 293-315. https://doi.org/10.1023/a:1015336929122
Lim, S. A., & Lee. J. (2016). Affective factors as a predictor of math achievement: Comparison of OECD high performing 10 countries. Journal of Educational Evaluation, 29(2), 357-382.
Muthen, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S. T., Yang, C. C., Wang, C. P., Kellam, S. G., Carlin, J. B., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3(4), 459-475. https://doi.org/10.1093/biostatistics/3.4.459
Muthen, B., & Muthen, L. (2019). Mplus: A general latent variable modeling program.
Nylund, K. L., Asparouhov, T., & Muthen, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. https://doi.org/10.1080/10705510701575396
Park, C. (2007). The trend in the Korean middle school students' affective variables toward mathematics and its effect on their mathematics achievements. The Mathematical Education, 46(1), 19-31.
Park, S., Chiu, W., & Won, D. (2017). Effects of physical education, extracurricular sports activities, and leisure satisfaction on adolescent aggressive behavior: A latent growth modeling approach. PLoS ONE, 14(4), e0174674. https://doi.org/10.1371/journal.pone.0174674
Park, S. H., & Sang, K. (2011). Characteristics of and factors affecting on students' attitude toward mathematics. School Mathematics, 13(4), 697-716.
Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing science, 12(1), 103-124. https://doi.org/10.1287/mksc.12.1.103
Reinecke, J., & Seddig, D. (2011). Growth mixture models in longitudinal research. Advances in Statistical Analysis, 95(4), 415-434. https://doi.org/10.1007/s10182-011-0171-4
Sang, K., Kwak, Y., Park, J., & Park, S. (2016). The trends in international mathematics and science study (TIMSS): Findings from TIMSS 2015 for Korea (RRE 2016-15-1). Korea Institute for Curriculum and Evaluation.
Sang, K. A., Kim K. H., Park S. W., Jeon S. K., Park, M. M., & Lee, J. W. (2020). An international comparative study on the trend of mathematical and scientific achievement: TIMSS 2019 (RRE 2020-10). Korea Institute for Curriculum and Evaluation.
Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
Shin, J. H. (2009). The impacts of prior learning and family environments on the attitudes toward math of applicants to the education center for gifted children in math. Korean Journal of Teacher Education, 25(2), 180-199.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.