Abstract
This study investigated the growth trajectory of academic achievement in Math and English among 519 students in a vocational senior high school in Taiwan. Covering the complete individual learning profile, our dataset included pre-enrollment variables, periodic test scores, and college entrance examination scores. We employed a group-based trajectory model that identified three homogenous subgroups with distinct trajectories of academic achievement in Math and English and demonstrated baseline predictive factors associated with these trajectories as well as relationships between different trajectories and students’ college entrance examination scores. Our analysis contributes to the literature in two ways. First, this study demonstrates that when school practices focus on improving or remediating the performance of students in the low-achievement group, the obvious decrease in performance of those in the middle is ignored. Such finding indicates the need for inclusive or specialized practices that enhance the performance of students in all groups. Second, our analysis reveals that pre-enrollment academic preparation appears to be a strong predictor of later academic performance as noted through the reproduction of pre-enrollment academic performance in students’ college entrance examination scores. Therefore, upon enrollment, schools should start interventions that reflect the needs of different groups of students.
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The data used in this study are extracted from the administrative data warehouse.
References
ACT (2012). The condition of college & career readiness. Iowa City, IA: Author.
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college (Report No. 2006-512-700). Government Printing Office, Washington, DC
Alhadabi, A., & Li, J. (2020). Trajectories of academic achievement in high schools: Growth mixture model. Journal of Educational Issues, 6(1), 140–165.
An, B. P. (2010). The relations between race, family characteristics, and where students apply to college. Social Science Research, 39(2), 310–323.
Anderson, J. R., Van Ryzin, M. J., & Doherty, W. J. (2010). Developmental trajectories of marital happiness in continuously married individuals: A group-based modeling approach. Journal of Family Psychology, 24(5), 587–596.
Astin, A. W., & Oseguera, L. (2012). Pre-college and institutional influences on degree attainment. In A. Seidman (Ed.), College student retention (2nd ed., pp. 119–145). Rowman & Littlefield Publishers.
Attewell, P., Heil, S., & Reisel, L. (2011). Competing explanations of undergraduate noncompletion. American Educational Research Journal, 45(3), 536–559.
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72, 187–206.
Blossfeld, H.-P., & Shavit, Y. (1993). Persisting barriers: Changes in educational opportunities in thirteen countries. In Y. Shavit & H.-P. Blossfeld (Eds.), Persistent inequality (pp. 1–24). Westview: Boulder.
Boscardin, C. K., Muthen, B., Francis, D. J., & Baker, E. L. (2008). Early identification of reading difficulties using heterogeneous developmental trajectories. Journal of Educational Psychology, 100(1), 192–208.
Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485–499.
Brown, P. (2013). Education, opportunity and the prospects for social mobility. British Journal of Sociology of Education, 34(5–6), 678–700.
Buckley, K., Fairman, K., Pogge, E., & Raney, E. (2021). Novel use of LMS data to predict online learning success in a pharmacy capstone course. American Journal of Pharmaceutical Education, 55(8), 1–8.
Cappella, E., & Weinstein, R. S. (2001). Turning around reading achievement: Predictors of high school students’ academic resilience. Journal of Educational Psychology, 93(4), 758–771.
Caprara, G. V., Fida, R., Vecchione, M., Del Bove, G., Vecchio, G. M., Barbaranelli, C., et al. (2008). Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement. Journal of Educational Psychology, 100, 525–534.
Casanova, P. F., Garcia- Linares, M. C., de la Torre, M. J., & Carpio, M. D. L. V. (2005). Influence of family and socio- demographic variables on students with low academic achievement. Educational Psychology, 25(4), 423–435.
Connell, A. M., & Frye, A. A. (2006). Growth mixture modelling in developmental psychology: Overview and demonstration of heterogeneity in developmental trajectories of adolescent antisocial behaviour. Infant and Child Development, 15(6), 609–621.
Considine, G., & Zappala, G. (2002). The influence of social and economic disadvantage in the academic performance of school students in Australia. Journal of Sociology, 35(2), 129–148.
Cunningham, M., Corprew, C. S., III., & Becker, J. E. (2009). Associations of future expectations, negative friends, and academic achievement in high-achieving African American adolescents. Urban Education, 44(3), 280–296.
Feldman, B. J., Masyn, K. E., & Conger, R. D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45(3), 652676.
Furnham, A., Chamorro-Premuzic, T., & McDougall, F. (2003). Personality, cognitive ability, and beliefs about intelligence as predictors of academic performance. Learning and Individual Differences, 14, 49–66.
Gore, P. A. (2006). Academic self-efficacy as a predictor of college outcomes: Two incremental validity studies. Journal of Career Assessment, 14, 92–115.
Greene, B. A., Miller, R. B., Crowson, H. M., Duke, B. L., & Akey, K. L. (2004). Predicting high school students' cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemporary Educational Psychology, 29(4), 462–482.
Henry, K., Knight, K., & Thornberry, T. (2012). School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood. Journal of Youth and Adolescence, 41(2), 156–166.
Hodis, F. A., Meyer, L. H., McClure, J., Weir, K. F., & Walkey, F. H. (2011). A longitudinal investigation of motivation and secondary school achievement using growth mixture modeling. Journal of Educational Psychology, 103(2), 312–323.
Hong, S., & You, S. (2012). Understanding Latino children’s heterogeneous academic growth trajectories: Latent growth mixture modeling approach. Journal of Educational Research, 105(4), 235–244.
Ingels, S. L., Dowd, K. L., Taylor, J. R., Bartot, V. H., Frankel, M. R., & Pulliam, P. A. (1995). Second follow-up: Transcript component data file user’s manual. U.S. Department of Education, Office of Educational Research and Improvement.
Jones, B. L., & Nagin, D. S. (2013). A note on a Stata plugin for estimating group-based trajectory models. Sociological Methods & Research, 42(4), 608–613.
Jonsson, J. O. (1993). Education, social mobility, and social reproduction in Sweden: patterns and changes. International Journal of Sociology, 23(1), 230.
Karbach, J., Gottschling, J., Spengler, M., Hegewald, K., & Spinath, F. M. (2013). Parental involvement and general cognitive ability as predictors of domain specific academic achievement in early adolescence. Learning and Instruction, 23, 43–51.
Karen, D. (2002). Changes in access to higher education in the United States: 1980–1992. Sociology of Education, 75(3), 191–210.
Kern, C. W., Fagley, N. S., & Miller, P. M. (1998). Correlates of college retention and GPA: Learning and study strategies, testwiseness, attitudes, and ACT. Journal of College Counseling, 1, 26–35.
Klassen, R. M. (2004). Optimism and realism: A review of self-efficacy from a cross-cultural perspective. International Journal of Psychology, 39, 205–230.
Komarraju, M., & Karau, S. J. (2005). The relationship between the big five personality traits and academic motivation. Personality and Individual Differences, 39, 557–567.
Kreisman, M. B. (2003). Evaluating academic outcomes of Head Start: An application of general growth mixture modeling. Early Childhood Research Quarterly, 15(2), 238–254.
Laidra, K., Pullmann, H., & Allik, J. (2007). Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and Individual Differences, 42(3), 441–451.
McLaughlin, K. A., & King, K. (2015). Developmental trajectories of anxiety and depression in early adolescence. Journal of Abnormal Child Psychology, 43(2), 311–323.
Messersmith, E. E., & Schulenberg, J. E. (2008). When can we expect the unexpected? Predicting educational attainment when it differs from previous expectations. Journal of Social Issues, 64(1), 195–212.
Ministry of Education. (2014). Digest of education statistics. Taiwan Ministry of Education.
Mishook, J., Gurantz, O., Borsato, G. N., Hewitson, M., Martinez, M., McGinnis, E., et al. (2012). College readiness indicator systems. Providence, RI: Annenberg Institute, Brown University. Retrieved from http://annenberginstitute.org/sites/default/files/VUE35a.pdf
Mody, A., Eshun-Wilson, I., Sikombe, K., Schwartz, S. R., Beres, L. K., Simbeza, S., Mukamb, N., Somwe, P., Bolton-Moore, C., Padian, N., Holmes, C. B., Sikazwe, L., & Geng, E. (2019). Longitudinal engagement trajectories and risk of death among new ART starters in Zambia: A group-based multi-trajectory analysis. PLoS Medicine, 16(10), e1002959.
Muthen, B. O. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables new opportunities for latent classlatent growth modeling. In L. M. Collins & A. G. Sayer (Eds.), Decade of behavior new methods for the analysis of change (pp. 291–322). Washington: American Psychological Association.
Nagin, D. (2009). Group-based modeling of development. Harvard University Press.
Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology, 6, 109–138.
Ou, S. R., & Reynolds, A. J. (2008). Predictors of educational attainment in the Chicago Longitudinal Study. School Psychology Quarterly, 23(2), 199–229.
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, 12(4), 1–13.
Patrick, M. E., & Schulenberg, J. E. (2011). How trajectories of reasons for alcohol use relate to trajectories of binge drinking: National panel data spanning late adolescence to early adulthood. Developmental Psychology, 47(2), 311–317.
Ready, D. D. (2010). Socioeconomic disadvantage, school attendance, and early cognitive development: The differential effects of school exposure. Sociology of Education, 53(4), 271–286.
Reyes, O., Gillock, K. L., Kobus, K., & Sanchez, B. (2000). A longitudinal examination of the transition into senior high school for adolescents from urban, low-income status, and predominantly minority backgrounds. American Journal of Community Psychology, 25, 519–544.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130, 261–288.
Rock, D., Owings, S., & Lee, R. (1994). Changes in math proficiency between eighth and tenth grades (Report No. NCES-93-455). National Center for Education Statistics, Washington, DC
Shanley, L. (2016). Evaluating longitudinal mathematics achievement growth: Modeling and measurement considerations for assessing academic progress. Educational Researcher, 45(6), 347–357.
Shin, S., Rachmatullah, A., Ha, M., & Lee, J. K. (2018). A longitudinal trajectory of science learning motivation in Korean high school students. Journal of Baltic Science Education, 17(4), 674–687.
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.
Smith, L. E., Maenner, M. J., & Seltzer, M. M. (2012). Developmental trajectories in adolescents and adults with autism: The case of daily living skills. Journal of the American Academy of Child and Adolescent Psychiatry, 51(6), 622–631.
Soares, A. P., Guisande, A. M., Almeida, L. S., & Paramo, F. M. (2009). Academic achievement in first- year Portuguese college students: The role of academic preparation and learning strategies. International Journal of Psychology, 44(3), 204–212.
Tschannen-Moran, M., & Barr, M. (2004). Fostering student learning: The relationship of collective teacher efficacy and student achievement. Leadership and Policy in Schools, 3(3), 189–209.
Wang, Y. C. (2013). Application of growth mixture model to heterogeneous trajectories of depressive modes of adolescents: A six-step strategic model development mechanism. Journal of Educational Research and Development, 9(4), 119–148.
Willms, J. D. (1999). Quality and inequality in children’s literacy: The effects on families, schools, and communities. In D. P. Keating & C. Hertzman (Eds.), Developmental health and the wealth of nations: Social, biological, and educational dynamics (pp. 72–93). Guilford Press.
Wolniak, G. C., & Engberg, M. E. (2010). Academic achievement in the first year of college: Evidence of the pervasive effects of the high school context. Research in Higher Education, 51(5), 451–467.
Yusuf, M. (2011). The impact of self-efficacy, achievement motivation, and self-regulated learning strategies on students’ academic achievement. Procedia—Social and Behavioral Sciences, 15, 2623–2626.
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Fu, Y.C., Chen, S.L., Quetzal, A.S. et al. Group-based trajectory model to analyze the growth of students’ academic performance: a longitudinal investigation at one Taiwanese high school. Asia Pacific Educ. Rev. 23, 515–526 (2022). https://doi.org/10.1007/s12564-022-09792-3
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DOI: https://doi.org/10.1007/s12564-022-09792-3