The role of academic achievement growth in school track recommendations

Author(s):

Daniel H. Caro Jenny Lenkeit Rainer Lehmann

Conference:

ECER 2009

Network:

23. Policy Studies and Politics of Education

Format:

Poster

Session Information

MC_POST, Main Conference Poster Session and Lunch Break

Posters will be displayed throughout the conference and submitters are asked to be present in both Poster Sessions to answer questions. Poster Session I: Tuesday, 12.15 - 13.30 Poster Session II: Wednesday 12.15 - 13.30

Time:

2009-09-29

12:15-13:15

Room:

Otkogon

Chair:

Contribution

Several European countries track students into different school types in the transition from primary to secondary schooling. Tracking decisions have profound and lasting consequences for future educational and professional careers of students and, therefore, their underlying mechanisms are of great interest to education researchers and policy makers. In Germany, although final decisions as to which secondary track the student enrolls rest on the parents, these are fundamentally shaped by recommendations issued by primary school teachers. Research has shown that teachers’ track recommendations are largely influenced by prior academic performance of students and that family’s socioeconomic status (SES) plays an additional role in that, for example, students whose parents have attained high levels of education or have high-prestige occupations are more likely to decide for the academic track (Abitur) irrespective of their school performance (Baumert & Schümer, 2001; Bos et al., 2004; Ditton, 2005; Ditton & Krüsken, 2006; Lehmann & Peek, 1997; Merkens & Wessel, 2002; Schnabel, Alfeld, Eccles, Köller, & Baumert, 2002). Boudon’s theoretical model is often invoked to frame SES and academic performance influences at this transitional point (Boudon, 1974; Maaz, Trautwein, Lüdtke, & Baumert, 2008). Next to academic achievement levels and family SES, past research has already shown the influence of aspects such as cultural capital (Condron, 2007), class and school composition characteristics (Tiedemann & Billmann-Mahecha, 2007; Trautwein & Baeriswyl, 2007), and gender (Updegraff, Eccles, O’Brien, 1996) on the tracking decision. The literature has, however, neglected the role of academic achievement growth in track recommendations, in spite of the increasing interest of educational researchers on growth rather than level in learning (Willet, 1988). Whereas achievement levels partly reflect natural ability differences among students as well as differences in other individual attributes, student progress measured through achievement growth reflects students’ capacity to acquire skills over their school careers and their potential for academic success and should therefore be reflected in the recommendation for a secondary school track. In this study we plan to examine whether teachers observe and reward achievement growth while evaluating students and making their secondary track recommendations.

Method

Data are drawn from the German study ELEMENT (N = 3,196). ELEMENT includes longitudinal information on academic achievement, socioeconomic background, and demographic characteristics of students in Berlin as they progress through grades four, five, and six. Statistical models are estimated in two stages. In the first stage, individual achievement growth is estimated from a linear growth model of math test scores (level 1) nested within students (level 2). The growth measure is equal to the empirical Bayes estimate (EB) of the growth rate, which penalizes the growth rate estimate for its reliability. In the second stage, the effect of the achievement growth (EB) on the track recommendation and math school grades is evaluated within a two-level logit model and a weighted least squares model, respectively, of students (level 1) nested within schools (level 2). Models control for the effect initial achievement levels, SES, and group reference characteristics.

Expected Outcomes

Some of our preliminary findings are: (1) math achievement growth rates significantly vary among students from grade 4 to 6; (2) math initial status and achievement growth are positively, albeit moderately, related (ρ=0.292); (3) math growth is valued by teachers while grading students with higher scores; (4) students growing faster in their math skills are more likely to obtain a recommendation for the college preparatory track; (5) math growth effects persist when initial achievement levels, SES, and class composition characteristics are controlled; (6) classes with high mean achievement levels, achievement growth and high SES status show negative effects on the probability to receive a recommendation for the academic track as well as receiving higher grades.

References

Baumert, J., & Schümer, G. (2001). Family background. Educational participation and the acquisition of competencies. In J. Baumert, E. Klieme, M. Neubrand, M. Prenzel, U. Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in Western society. New York: Wiley. Bos, W., Voss, A., Lankes, E.-M., Schwippert, K., Thiel, O., & Valtin, R. (2004). Teachers’ recommendations for student tracking at the end of fourth grade. In W. Bos, E.-M. Lankes, M. Prenzel, K. Schwippert, R. Valtin, & G. Walther (Eds.), IGLU. Einige Länder der Bundesrepublik Deutschland im nationalen und internationalen Vergleich (pp. 191–220). Münster, Germany: Waxmann. Caro, D., McDonald, J. T., & Willms, J. D. Socioeconomic status and academic achievement trajectories from childhood to adolescence. Manuscript submitted for publication. Caro. D. & Lehmann, R. Measuring socioeconomic status and its gradient effect on student achievement in Hamburg. Manuscript submitted for publication. Caro. D. & Lehmann, R. Achievement inequalities in Hamburg schools: How do they change as students get older? Manuscript submitted for publication. Caro, D., Schnabel, K., & Eccles, J. Socioeconomic background, education, and labor force outcomes. Manuscript submitted for publication. Condron, J. (2007). Explaining ascriptive inequalities in early childhood reading group placement. Social Problems, 54, 139–160. Ditton, H. (2005). The contribution of family and school to the reproduction of educational inequality. In H. G. Holtappels & K. Ho¨hmann (Eds.), Schulentwicklung und Schulwirksamkeit. Systemsteuerung, Bildungschancen und Entwicklung der Schule. 30 Jahre Institut fu¨r Schulentwicklungsforschung (pp. 121–130). Weinheim, Germany: Juventa. Ditton, H., & Krüsken, J. (2006): Der Übergang von der Grundschule in die Sekundarstufe I. Zeitschrift für Erziehungswissenschaft, 9(3), 348-372. Lehmann, R.H., & Peek, R. (1997). Aspekte der Lernausgangslage und der Lernentwicklung von Schülerinnen und Schülern, die im Schuljahr 1996/97 eine fünfte Klasse an Hamburger Schulen besuchten. Hamburg: Behörde für Schule, Jugend und Berufsbildung. Lehmann, R. & Lenkeit, J. (2008). ELEMENT. Erhebung zum Lese- und Mathematikverständnis - Entwicklungen in den Jahrgangsstufen 4 bis 6 in Berlin. Abschlussbericht über die Untersuchungen 2003, 2004 und 2005 an Berliner Grundschulen und grundständigen Gymnasien. Humboldt Universität zu Berlin Maaz, K., Trautwein, U., Lüdtke, O., & Baumert, J. (2008). Educational transitions and differential learning environments: How explicit between-school tracking contributes to social inequality in educational outcomes. Child Development Perspectives, 2(2), 99–106. Merkens, H, & Wessel, A. (2002). Zur Genese von Bildungsentscheidungen. Eine empirische Studie in Berlin und Brandenburg. Jugendforschung aktuell 7. Hohengehren: Schneider Verlag. Schiefele, W. Schneider, et al. (Eds.), PISA 2000. Basiskompetenzen von Schu¨lerinnen und Schu¨lern im internationalen Vergleich (pp. 323–407). Opladen, Germany: Leske + Budrich. Schnabel, K., Alfeld, C., Eccles, J., Köller, O., & Baumert, J. (2002). Parental influence on students’ educational choices in the United States and Germany: Different ramifications—same effect? Journal of Vocational Behavior, 60, 178–198. Tiedemann, J., & Billmann-Mahecha, E. (2007). „Zum Einfluss von Migration und Schulklassenzugehörigkeit auf die Übergangsempfehlung für die Sekundarstufe I“ Zeitschrift für Erziehungswissenschaft, 10(1), 108-120. Trautwein, U., & Baeriswyl, F. (2007). “When high-achieving classmates put students at a disadvantage: Reference group effects at the transition to secondary schooling” Zeitschrift für Pädagogische Psychologie, 21(2), 119–133. Updegraff, K.A., Eccles, J.S., & O’Brien, B.B. (1996). “Course-enrollment as self-regulatory behavior: Who takes optional high school math courses” Learning and Individual Differences, 8(3), 239-259. Willet, J.B. (1988). Questions and answers in the measurement of change. Review of Research in Education, 15, 345–422.

Author Information

Daniel H. Caro

International Max Planck Research School LIFE

Berlin

Jenny Lenkeit

University of Hamburg

Education

Hamburg

Rainer Lehmann

Humboldt-Universität zu Berlin

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