Advertisement

An Intensive Longitudinal Study of the Development of Student Achievement over Two Years (LUISE)

  • Gizem HülürEmail author
  • Fidan Gasimova
  • Alexander Robitzsch
  • Oliver Wilhelm
Chapter
Part of the Methodology of Educational Measurement and Assessment book series (MEMA)

Abstract

Educational researchers have long been interested in quantifying the amount of change in student achievement as a result of schooling. In this paper, we present an intensive longitudinal study of student achievement and cognitive ability over a time span of two academic years, from the beginning of ninth grade until the end of tenth. One hundred and twelve students participated in the intensive longitudinal study, which consisted of 44 testing sessions. A control group of 113 students participated only in the pretest and posttest. We provide descriptive results for the trajectories of German language and mathematics achievement in different domains and report comparisons between the study and control groups. Taken together, our findings reveal that student achievement increased over the course of two academic years, with effect sizes amounting to about 60–80 % of a full standard deviation unit for German achievement, and to about two thirds to a full standard deviation unit for mathematics achievement. Furthermore, the findings did not reveal any evidence for higher increases in student achievement for the study group. We conclude that intensive longitudinal studies allow for examining change in student achievement over shorter time spans without confounding the findings with learning effects related to retest and discuss open questions for future research.

Keywords

Student achievement Longitudinal Language achievement Mathematics Secondary school 

Notes

Acknowledgments

This research was supported by the Institute for Educational Progress (IQB), Humboldt University, Berlin, Germany, and by a grant from the German Research Foundation (DFG), awarded to Oliver Wilhelm and Alexander Robitzsch (WI 2667/7-1) in the Priority Program “Competence Models for Assessing Individual Learning Outcomes and Evaluating Educational Processes” (SPP 1293). Gizem Hülür and Fidan Gasimova were predoctoral fellows of the International Max Planck Research School: “The Life Course: Evolutionary and Ontogenetic Dynamics (LIFE)”.

References

  1. Adams, R., & Wu, M. (2002). PISA 2000 Technical Report. Paris: OECD.Google Scholar
  2. Algina, J., Keselman, H. J., & Penfield, R. D. (2005). Effect sizes and their intervals: The two-level repeated measures case. Educational and Psychological Measurement, 65, 241–258. doi: 10.1177/0013164404268675.CrossRefGoogle Scholar
  3. Aunola, K., Leskinen, E., Onatsu-Arvilommi, T., & Nurmi, J.-E. (2002). Three methods for studying developmental change: A case of reading skills and self-concept. British Journal of Educational Psychology, 72, 343–364. doi: 10.1348/000709902320634447.CrossRefGoogle Scholar
  4. Bast, J., & Reitsma, P. (1998). Analyzing the development of individual differences in terms of Matthew effects in reading: Results from a Dutch longitudinal study. Developmental Psychology, 34, 1373–1399. doi: 10.1037/0022-0663.97.3.299.CrossRefGoogle Scholar
  5. Baumert, J., Bos, W., Lehmann, R. (Eds.). (2000). TIMSS/III. Dritte Internationale Mathematik- und Naturwissenschaftsstudie: Mathematische und naturwissenschaftliche Bildung am Ende der Schullaufbahn [Third International Mathematics and Science Study: Mathematical and scientific literacy at the end of the school career]. Band 1: Mathematische und naturwissenschaftliche Grundbildung am Ende der Pflichtschulzeit. Opladen: Leske + Budrich.Google Scholar
  6. Beck, B., & Klieme, K. (Eds.). (2007). Sprachliche Kompetenzen, Konzepte und Messung: DESI-Studie (Deutsch Englisch Schülerleistungen International) [Language competencies, concepts and measurements: DESI-Study (German English student performance international)]. Weinheim: Beltz.Google Scholar
  7. Becker, M., Lüdtke, O., Trautwein, U., & Baumert, J. (2006). Leistungszuwachs in Mathematik: Evidenz für einen Schereneffekt im mehrgliedrigen Schulsystem [Growth in mathematics achievement: Evidence for a scissor effect in a three-tracked school system]? Zeitschrift für Pädagogische Psychologie, 20, 233–242. doi: 10.1024/1010-0652.20.4.233.CrossRefGoogle Scholar
  8. Bloom, H., Hill, C., Rebeck Black, A., Lipsey, M. (2008). Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. Retrieved from MDRC Working Papers on Research Methodology. http://www.mdrc.org/sites/default/files/full_473.pdf.
  9. Bremerich-Vos, A., & Böhme, K. (2009). Lesekompetenzdiagnostik: Die Entwicklung eines Kompetenzstufenmodells für den Bereich Lesen [Assessment of reading competence]. In A. Bremerich-Vos, D. Granzer, & O. Köller (Eds.), Bildungsstandards Deutsch und Mathematik (pp. 219–249). Weinheim: Beltz.Google Scholar
  10. Brunner, M. (2006). Mathematische Schülerleistung: Struktur, Schulformunterschiede und Validität [Student achievement in mathematics: Structure, school type differences and validity] (Doctoral dissertation, Humboldt-Universität Berlin, Berlin). Retrieved from http://edoc.hu-berlin.de/dissertationen/brunner-martin-2006-02-08/PDF/brunner.pdf.
  11. Cahan, S., & Cohen, N. (1989). Age versus schooling effects on intelligence development. Child Development, 60, 1239–1249. doi: 10.1111/j.1467-8624.1989.tb03554.x.CrossRefGoogle Scholar
  12. Compton, D. L. (2003). Modeling the relationship between growth in rapid naming speed and growth in decoding skill in first-grade children. Journal of Educational Psychology, 95, 225–239. doi: 10.1037/0022-0663.95.2.225.CrossRefGoogle Scholar
  13. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.Google Scholar
  14. Ehmke, T., Blum, W., Neubrand, M., Jordan, A., & Ulfig, F. (2006). Wie verändert sich die mathematische Kompetenz von der neunten zur zehnten Klassenstufe [How does mathematical competence change from ninth until tenth grade]? In P. I. S. A.-K. Deutschland (Ed.), PISA 2003. Untersuchungen zur Kompetenzentwicklung im Verlauf eines Schuljahres (pp. 63–85). Münster: Waxmann.Google Scholar
  15. Ferrer, E., McArdle, J. J., Shaywitz, B. A., Holahan, J. N., Marchione, K., & Shaywitz, S. E. (2007). Longitudinal models of developmental dynamics between reading and cognition from childhood to adolescence. Developmental Psychology, 43, 1460–1473. doi: 10.1037/0012-1649.43.6.1460.CrossRefGoogle Scholar
  16. Grimm, K. J. (2008). Longitudinal associations between reading and mathematics. Developmental Neuropsychology, 33, 410–426. doi: 10.1080/87565640801982486.CrossRefGoogle Scholar
  17. 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, 15, 541–563. doi: 10.1080/10705510802338983.CrossRefGoogle Scholar
  18. Hülür, G., Wilhelm, O., & Robitzsch, A. (2011a). Intelligence differentiation in early childhood. Journal of Individual Differences, 32, 170–179. doi: 10.1027/1614-0001/a000049.CrossRefGoogle Scholar
  19. Hülür, G., Wilhelm, O., & Robitzsch, A. (2011b). Multivariate Veränderungsmodelle für Schulnoten und Schülerleistungen in Deutsch und Mathematik [Multivariate change models for student achievement and school grades in German and mathematics]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 43, 173–185. doi: 10.1026/0049-8637/a000051.CrossRefGoogle Scholar
  20. Hülür, G., Wilhelm, O., & Schipolowski, S. (2011c). Prediction of self-reported knowledge with over-claiming, fluid and crystallized intelligence and typical intellectual engagement. Learning and Individual Differences, 21, 742–746. doi: 10.1016/j.lindif.2011.09.006.CrossRefGoogle Scholar
  21. Jude, N., Klieme, E., Eichler, W., Lehmann, R., Nold, G., Schröder, K., et al. (2008). Strukturen sprachlicher Kompetenzen [Structure of language competencies]. In DESI-Konsortium (Ed.), Unterricht und Kompetenzerwerb in Deutsch und Englisch (pp. 191–201). Beltz: Weinheim.Google Scholar
  22. Klieme, E. (2000). Fachleistungen im voruniversitären Mathematik- und Physikunterricht: Theoretische Grundlagen, Kompetenzstufen und Unterrichtsschwerpunkte [Academic performance in pre-university mathematics and physics: Theoretical foundations, competence levels, and core themes of instruction]. In J. Baumert, W. Bos, & R. Lehmann (Eds.), TIMSS/III. Dritte Internationale Mathematik- und Naturwissenschaftsstudie—Mathematische und naturwissenschaftliche Bildung am Ende der Schullaufbahn: Band 2. Mathematische und physikalische Kompetenzen am Ende der gymnasialen Oberstufe (pp. 57–128). Opladen: Leske + Budrich.Google Scholar
  23. KMK (Standing Conference of the Ministers of Education and Cultural Affairs of the States in the Federal Republic of Germany). (Ed.). (2005). Bildungsstandards der Kultusministerkonferenz, Erläuterungen zur Konzeption und Entwicklung. Beschluss vom 16.12.2004 [Educational standards, conception and development: Resolution approved by the Standing Conference on 16 December 2004]. Neuwied: Luchterhand.Google Scholar
  24. Michaelides, M. P. (2010). A review of the effects on IRT item parameter estimates with a focus on misbehaving common items in test equating. Frontiers in Quantitative Psychology and Measurement, 1(167). doi: 10.3389/fpsyg.2010.00167.
  25. Monseur, C., & Berezner, A. (2007). The computation of equating errors in international surveys in education. Journal of Applied Measurement, 8, 323–335.Google Scholar
  26. Muthén, B. O., & Khoo, S. T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, 10, 73–101. doi: 10.1016/S1041-6080(99)80135-6.CrossRefGoogle Scholar
  27. Muthén, B. O., Khoo, S.-T., & Goff, G. N. (1997). Multidimensional description of subgroup differences in mathematics achievement data from the 1992 National Assessment of Educational Progress. Los Angeles: CRESST/University of California.CrossRefGoogle Scholar
  28. OECD (Organisation for Economic Co-operation and Development). (1999). Measuring student knowledge and skills: A new framework for assessment. Paris: Author.Google Scholar
  29. Oller, J. W. (1976). Evidence for a general language proficiency factor: An expectancy grammar. Die Neueren Sprachen, 75, 165–174.Google Scholar
  30. Rescorla, L., & Rosenthal, A. S. (2004). Growth in standardized ability and achievement test scores from third to tenth grade. Journal of Educational Psychology, 96, 85–96.CrossRefGoogle Scholar
  31. Retelsdorf, J., & Möller, J. (2008). Entwicklungen von Lesekompetenz und Lesemotivation: Schereneffekte in der Sekundarstufe [Developments in reading literacy and reading motivation: Achievement gaps in secondary school]? Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 40, 179–188. doi: 10.1026/0049-8637.40.4.179.CrossRefGoogle Scholar
  32. Sang, F., Schmitz, B., Vollmer, H. J., Baumert, J., & Roeder, P. M. (1986). Models of second language competence: A structural equation approach. Language Testing, 3, 54–79. doi: 10.1177/026553228600300103.CrossRefGoogle Scholar
  33. Schipolowski, S., Böhme, K., Neumann, D., Vock, M., Pant, H. A. (2010). Bereitstellung eines pilotierten und normierten Aufgabenpools für kompetenzbasierte Vergleichsarbeiten im Fach Deutsch in der 8. Jahrgangsstufe im Schuljahr 2009/2010 [Provision of a piloted and standardized item pool for competence-based assessments in German]. (Technical Report). Berlin: IQB.Google Scholar
  34. Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2(27). doi: 10.3389/fnagi.2010.00027.
  35. Schmitz, B. (2006). Advantages of studying processes in educational research. Learning and Instruction, 16, 433–449. doi: 10.1016/j.learninstruc.2006.09.004.CrossRefGoogle Scholar
  36. Shin, S.-K. (2005). Did they take the same test? Examinee language proficiency and the structure of language tests. Language Testing, 22, 31–57. doi: 10.1191/0265532205lt296oa.CrossRefGoogle Scholar
  37. Siegler, R. S., & Svetina, M. (2006). What leads children to adopt new strategies? A microgenetic/cross sectional study of class inclusion. Child Development, 77, 997–1015. doi: 10.1111/j.1467-8624.2006.00915.x.CrossRefGoogle Scholar
  38. Strathmann, A., & Klauer, K. J. (2010). Lernverlaufsdiagnostik: Ein Ansatz zur längerfristigen Lernfortschrittsmessung [Measurement of learning trajectories: An approach to long-term measurement of learning progress]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 42, 111–122.CrossRefGoogle Scholar
  39. Strathmann, A., Klauer, K. J., & Greisbach, M. (2010). Lernverlaufsdiagnostik. Dargestellt am Beispiel der Rechtschreibkompetenz in der Grundschule [Measurement of learning trajectories. The development of writing competency in primary school]. Empirische Sonderpädagogik, 2, 64–77.Google Scholar
  40. Walberg, H., & Tsai, S. (1983). Matthew effects in education. American Educational Research Journal, 20, 359–373. doi: 10.3102/00028312020003359.Google Scholar
  41. Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427–445. doi: 10.1007/BF02294627.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gizem Hülür
    • 1
    Email author
  • Fidan Gasimova
    • 2
  • Alexander Robitzsch
    • 3
  • Oliver Wilhelm
    • 2
  1. 1.University of ZurichZurichSwitzerland
  2. 2.Ulm UniversityUlmGermany
  3. 3.Leibniz Institute for Science and Mathematics Education (IPN)KielGermany

Personalised recommendations