Skip to main content
Log in

STUDENTS’ COMPETENCIES IN WORKING WITH FUNCTIONS IN SECONDARY MATHEMATICS EDUCATION—EMPIRICAL EXAMINATION OF A COMPETENCE STRUCTURE MODEL

  • Published:
International Journal of Science and Mathematics Education Aims and scope Submit manuscript

Abstract

In the subject matter of functional relationships, a student’s ability to translate from one form of representation to another is seen as a central competence. In the course of the HEUREKO project (heuristic work with representations of functional relationships and the diagnosis of mathematical competencies of students), a theoretical competence structure model comprising five competence dimensions was developed. These are based on four types of representation (graph, numerical table, algebraic equation and situational description) and correspond to the skill to translate from one type of representation to another. The following study was aimed to examine the facets of the model. The 5-dimensional model was empirically tested with a sample of N = 645 students of grades 9 and 10. This was accomplished by comparing competing item response models with regard to model fit using information criteria measures. In comparison with other possible model structures, our postulated 5-dimensional model showed the best model fit, suggesting that all translations are relevant for competence assessment and development. Furthermore, in order to allow for the identification of structural components of cognitive actions, the employed tasks used in the empirical testing were divided into categories with regard to the different demands of cognitive action. Our results suggest that cognitive actions may have a specific dimensional structure. These findings can contribute to a better diagnostic approach concerning specific strengths and weaknesses and can therefore foster students’ competencies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adu-Gaymfi, K. (2007). Connections among representations: The nature of students’ coordinations on a linear function task. Doctoral dissertation, Raleigh, NC

  • Adu-Gyamfi, K., Stiff, L. V. & Bossé, M. J. (2012). Lost in translation: Examining translation errors associated with mathematical representations. School Science and Mathematics, 112(3), 159–170.

    Article  Google Scholar 

  • Ainsworth, S. E. (1999). The functions of multiple representations. Computers & Education, 33, 131–152.

    Article  Google Scholar 

  • Ainsworth, S. E., Bibby, P. A. & Wood, D. J. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11(1), 25–62.

    Article  Google Scholar 

  • Biggs, J. & Tang, C. (2007). Teaching for quality learning at university (3rd ed.). Buckingham: Society for Research into Higher Education and Open University Press.

    Google Scholar 

  • Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 397–479). Reading: Addison-Wesley.

    Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Book  Google Scholar 

  • Borromeo-Ferri, R. (2006). Theoretical and empirical differentiations of phases in the modelling process. Zentralblatt für Didaktik der Mathematik, 38(2), 86–95.

    Article  Google Scholar 

  • Bossé, M. J., Adu-Gyamfi, K. & Cheetham, M. (2011). Assessing the difficulty of mathematical translations: synthesizing the literature and novel findings. International Electronic Journal of Mathematics Education, 6(3), 113–133.

    Google Scholar 

  • Bruder, R. & Brückner, A. (1989). Zur Beschreibung von Schülertätigkeiten im Mathematikunterricht—ein allgemeiner Ansatz. Pädagogische Forschung, Berlin, 30(6), 72–82.

    Google Scholar 

  • Confrey, J. & Smith, E. (1995). Splitting, covariation, and their role in the development of exponential functions. Journal for Research in Mathematics Education, 26(1), 66–86.

    Article  Google Scholar 

  • Dreyfus, T. & Eisenberg, T. (1981). Function concepts: Intuitive baseline. In C. Comiti (Eds.). Proceedings of the fifth international conference of the International Group for the Psychology of Mathematics Education (pp. 183–188). Grenoble: IGPME.

  • Duval, R. (1993). Registres de représentations sémiotiques et fonctionnement cognitif de la pensée. Annales de Didactique et de Sciences Cognitives. Strasbourg: ULP, IREM 5, 37–65.

  • Ericsson, K. A. & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215–251.

    Article  Google Scholar 

  • Ford, S.J. (2008). The effect of graphing calculators and a three-core representation curriculum on college students’ learning of exponential and logarithmic functions. Doctoral dissertation, Raleigh, NC.

  • Gass, S. M. & Mackey, A. (2000). Stimulated recall methodology in second language research. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gagatsis, A. & Elia, I. (2004). The effects of different modes of representation on mathematical problem solving. In M.J. Hoines & A.B. Fuglestad (Eds.) Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education: Vol. 2 (pp. 447–454). Bergen: Bergen University College

  • Goldin, G. A. (1998). Representational systems, learning and problem solving in mathematics. Journal of Mathematical Behavior, 17(2), 137–165.

    Article  Google Scholar 

  • Gonzalez, E. & Rutkowski, L. (2010). Principles of multiple matrix booklet designs and parameter recovery in large-scale assessments. IERI Monograph Series. Issues and Methodologies in Large-Scale Assessments, 3, 125–156.

    Google Scholar 

  • Hattikudur, S., Prather, R. W., Asquith, P., Alibali, M. W., Knuth, E. J. & Nathan, M. (2012). Constructing graphical representations: Middle schoolers’ intuitions and developing knowledge about slope and Y-intercept. School Science and Mathematics, 112(4), 230–240.

    Article  Google Scholar 

  • Janvier, C. (1987). Translation processes in mathematics education. In C. Janvier (Ed.), Problems of representation in mathematics learning and problem solving (pp. 27–32). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kaput, J.J. (1985). Representation and problem solving, methodological issues related to modelling. In. E.A. Silver (Eds.), Teaching and learning mathematical problem solving: Multiple research perspectives (pp. 381–398). Hillsdale, NJ: Erlbaum

  • Keller, B. A. & Hirsch, C. R. (1998). Student preferences for representations of functions. International Journal of Mathematical Education in Science and Technology, 29(1), 1–17.

    Article  Google Scholar 

  • Kerslake, D. (1981). Graphs. In K. M. Hart (Ed.), Children’s understanding of mathematics: 11–16 (pp. 120–136). London: John Murray.

    Google Scholar 

  • Klieme, E., Hartig, J. & Rauch, D. (2008). The concept of competence in educational contexts. In J. Hartig, E. Klieme & D. Leutner (Eds.), Assessment of competencies in educational contexts (pp. 3–22). Göttingen: Hogrefe.

    Google Scholar 

  • Leinhardt, G., Zaslavsky, O. & Stein, M. K. (1990). functions, graphs, and graphing: tasks, learning, and teaching. Review of Educational Research, 60(1), 1–64.

    Article  Google Scholar 

  • Markovits, Z., Eylon, B. & Bruckheimer, M. (1986). Functions today and yesterday. For the Learning of Mathematics, 6(2), 18–28.

    Google Scholar 

  • Moosbrugger, H. & Kelava, A. (Eds.). (2007). Testtheorie und Fragebogenkonstruktion. Heidelberg: Springer.

    Google Scholar 

  • Muthén, L. K. & Muthén, B. O. (1998-2010). Mplus user’s guide (6th ed.). Los Angeles: Muthén & Muthén.

    Google Scholar 

  • Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.

    Article  Google Scholar 

  • Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. (Copenhagen, Danish Institute for Educational Research), expanded edition (1980) with foreword and afterword by B.D. Wright. Chicago: The University of Chicago Press.

  • Schnotz, W. & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13, 141–156.

    Article  Google Scholar 

  • Schoenfeld, A. H., Smith, J. & Arcavi, A. (1993). Learning: The microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 4, pp. 55–176). Hillsdale: Erlbaum.

    Google Scholar 

  • Schwarz, B. & Eisenberg, T. (1993). Measuring integration of information in multirepresentational software. Interactive Learning Environments, 3(3), 177–198.

    Article  Google Scholar 

  • Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13, 227–237.

    Article  Google Scholar 

  • Sierpinska, A. (1992). On understanding the notion of function. In E. Dubinsky & G. Harel (Eds.), The concept of function: Aspects of epistemology and pedagogy (pp. 25–58). Washington, DC: Mathematical Association of America.

    Google Scholar 

  • Swan, M. (1985). The language of functions and graphs. Nottingham: Shell Centre for Mathematical Education.

    Google Scholar 

  • Thomas, M. O. J., Wilson, A. J., Corballis, M. C., Lim, V. K. & Yoon, C. (2010). Evidence from cognitive neuroscience for the role of graphical and algebraic representations in understanding function. ZDM: The International Journal on Mathematics Education, 42(6), 607–619.

    Article  Google Scholar 

  • Thompson, P. W. (1994). Images of rate and operational understanding of the fundamental theorem of calculus. Educational Studies in Mathematics, 26, 229–274.

    Article  Google Scholar 

  • Wilmot, D. B., Schönfeld, A., Wilson, M., Champney, D. & Zahner, W. (2011). Validating a learning progression in mathematical functions for college readiness. Mathematical Thinking and Learning, 13(4), 259–291.

    Article  Google Scholar 

  • Wilson, M. (2008). Cognitive diagnosis using item response models. Journal of Psychology, 216(2), 74–88.

    Google Scholar 

  • Wu, M. L., Adams, R. J., Wilson, M. & Haldane, S. (2007). ConQuest Version 2.0. St. Paul: Assessment Systems Corporation.

    Google Scholar 

  • Zaslavsky, O. (1987). Conceptual obstacles in the learning of quad atic functions. Doctoral dissertation, Technion, Haifa, Israel.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Regina Bruder.

Appendix

Appendix

Table 4 AIC, BIC, and sample size adjusted BIC of the 1 PL and 2 PL model (data set of 121 items) (concerning the five-dimensional competence structure model)
Table 5 Chi-square difference test: comparison of the 1PL and 2PL models for the analysis of the change of representation
Table 6 BIC values of the models to be compared (data set of 108 items)
Table 7 Correlation of the various dimensions (data set of 108 items)
Table 8 Chi-square difference test: comparison of the 1PL and 2PL models for an analysis of the elements of cognitive action
Table 9 BIC values of the 1PL model (data set of 131 items)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nitsch, R., Fredebohm, A., Bruder, R. et al. STUDENTS’ COMPETENCIES IN WORKING WITH FUNCTIONS IN SECONDARY MATHEMATICS EDUCATION—EMPIRICAL EXAMINATION OF A COMPETENCE STRUCTURE MODEL. Int J of Sci and Math Educ 13, 657–682 (2015). https://doi.org/10.1007/s10763-013-9496-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10763-013-9496-7

Key words

Navigation