Abstract
Prompting students to construct multiple solutions for modelling problems with vague conditions has been found to be an effective way to improve students’ performance on interest-oriented measures. In the current study, we investigated the influence of this teaching element on students’ performance. To assess the impact of prompting multiple solutions in mathematics instruction compared with the prompting of a single solution, we conducted an experimental study with 144 ninth graders from six German classes from middle track schools. We had two experimental groups: In one experimental group, students were required to provide two solutions for modelling problems related to the topic of Pythagoras’ theorem; in the other group, they were asked to find one solution for each problem. Students’ performance in solving tasks with and without a connection to the real world was assessed before and after a five-lesson teaching unit. In addition, the number of solutions developed and students’ experience of competence were assessed with a questionnaire during the teaching unit. The findings showed that, similar to previous studies, prompting students to find multiple solutions does not improve their performance directly. However, using path analysis, we found indirect effects of the treatment on students’ performance via the number of solutions they developed and their experience of competence.
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Notes
In the multiple-solution condition, the number of students who reported developing more than two solutions varied between 9 and 16 % for each of four problems presented to the class.
References
Achmetli, K., Schukajlow, S., & Krug, A. (2014). Effects of prompting students to use multiple solution methods while solving real-world problems on students’ self-regulation. In C. Nicol, S. Oesterle, P. Liljedahl, & D. Allan (Eds.), Proceedings of the joint meeting of PME 38 and PME-NA 36 (Vol. 2, pp. 1–8). Vancouver, Canada: PME.
Ball, D. L. (1993). With an eye on the mathematical horizon: Dilemmas of teaching elementary school mathematics. The Elementary School Journal, 93, 373–397.
Bandura, A. (2003). Self-efficacy: The exercise of control (6. Printing. ed.). New York: Freeman.
Baumert, J., Kunter, M., Blum, W., Brunner, M., Dubberke, T., Jordan, A., et al. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47, 133–180.
Becker, J. P., & Shimada, S. (Eds.). (1997). The open-ended approach: A new proposal for teaching mathematics. Reston, VA: National Council of Teachers of Mathematics.
Blum, W. (2011). Can modelling be taught and learnt? Some answers from empirical research. In G. Kaiser, W. Blum, R. Borromeo Ferri, & G. Stillman (Eds.), Trends in the teaching and learning of mathematical modelling - Proceedings of ICTMA14 (pp. 15–30). New York: Springer.
Blum, W., Galbraith, P. L., Henn, H.-W., & Niss, M. (2007). Modelling and applications in mathematics education. The 14th ICMI study. New York: Springer.
Blum, W., & Leiss, D. (2007). How do students and teachers deal with mathematical modelling problems? The example sugarloaf and the DISUM project. In C. Haines, P. L. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modelling (ICTMA 12): Education, engineering and economics (pp. 222–231). Chichester: Horwood.
Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231.
Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum.
Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15, 1–40.
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning and instruction: Essays in honor of Robert Glaser (pp. 453–492). Hillsdale, NJ: Erlbaum.
Deci, E. L., & Ryan, A. M. (2000). The “what” and “why” of goal pursuits: Human needs and the selfdetermination of behavior. Psychological Inquiry, 11(4), 227–268.
Dhombres, J. (1993). Is one proof enough? Travels with a mathematician of the baroque period. Educational Studies in Mathematics, 24(24), 401–419.
Dörnyei, Z. (2005). Theaching and researching motivation. Harlow: Longman.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.
Galbraith, P. L., & Stillman, G. (2001). Assumptions and context: Pursuing their role in modelling activity. In J. Matos, W. Blum, K. Houston, & S. Carreira (Eds.), Modelling and mathematics education, ICTMA 9: Applications in science and technology (pp. 300–310). Chichester: Horwood Publishing.
Galbraith, P. L., & Stillman, G. (2006). A framework for identifying student blockages during transitions in the modelling process. ZDM The International Journal on Mathematics Education, 38(2), 143–162.
Goetz, T., Frenzel, A. C., Pekrun, R., Hall, N. C., & Lüdtke, O. (2007). Between- and within-domain relations of students’ academic emotions. Journal of Educational Psychology, 99, 715–733.
Große, C. S. (2014). Mathematics learning with multiple solution methods: Effects of types of solutions and learners’ activity. Instructional Science, 42, 1–31.
Große, C. S., & Renkl, A. (2006). Effects of multiple solution methods mathematics learning. Learning and Instruction, 16(2), 122–138.
Guberman, R., & Leikin, R. (2013). Interesting and difficult mathematical problems: Changing teachers’ views by employing multiple-solution tasks. Journal of Mathematics Teacher Education, 16(1), 33–56.
Hannula, M. S. (2006). Motivation in mathematics: Goals reflected in emotions. Educational Studies in Mathematics, 63, 165–178.
Hänze, M., & Berger, R. (2007). Cooperative learning, motivational effects, and student characteristics: An experimental study comparing cooperative learning and direct instruction in 12th grade physics classes. Learning and Instruction, 17(1), 29–41.
Hattie, J. (2009). Visible learning: A synthesis of meta-analyses relating to achievement. London: Routledge.
Hattie, J., Biggs, J. B., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136.
Heinze, A., Reiss, K., & Rudolph, F. (2005). Mathematics achievement and interest in mathematics from a differential perspective. ZDM The International Journal on Mathematics Education, 37(3), 212–220.
Hiebert, J., Gallimore, R., Garnier, H., Givvin, K. B., Hollingsworth, H., Jacobs, J., et al. (2003). Teaching mathematics in seven countries. Results from the TIMSS 1999 video study. Washington, DC: NCES.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
Klavir, R., & Hershkovitz, S. (2008). Teaching and evaluating ‘open-ended’ problems. International Journal for Mathematics Teaching and Learning, 20(5), 23.
Kline, R. B. (2005). Principles and practice of structural equation modeling. New York, NY: Guilford Press.
Krapp, A. (2005). Basic needs and the development of interest and intrinsic motivational orientations. Learning and Instruction, 15, 381–395.
Kunter, M., Baumert, J., & Köller, O. (2007). Effective classroom management and the development of subject-related interest. Learning and Instruction, 17, 494–509.
Leikin, R., & Levav-Waynberg, A. (2007). Exploring mathematics teacher knowledge to explain the gap between theory-based recommendations and school practice in the use of connecting tasks. Educational Studies in Mathematics, 66(3), 349–371.
Leiss, D., Schukajlow, S., Blum, W., Messner, R., & Pekrun, R. (2010). The role of the situation model in mathematical modelling – task analyses, student competencies, and teacher interventions. Journal für Mathematikdidaktik, 31(1), 119–141.
Levav-Waynberg, A., & Leikin, R. (2012). The role of multiple solution tasks in developing knowledge and creativity in geometry. Journal of Mathematical Behavior, 31, 73–90.
Lin, C.-Y., Becker, J., Ko, Y.-Y., & Byun, M.-R. (2013). Enhancing pre-service teachers’ fraction knowledge through open approach instruction. Journal of Mathematical Behavior, 32(3), 309–330.
Maaß, K. (2010). Classification scheme for modelling tasks. Journal für Mathematikdidaktik, 31(2), 285–311.
Malmivuori, M.-L. (2006). Affect and self-regulation. Educational Studies in Mathematics, 63, 149–164.
Marcou, A., & Lerman, S. (2007). Changes in students’ motivational beliefs and performance in a self-regulated mathematical problem-solving environment. In D. Pitta-Pantazi, & G. Philippou (Eds.), Proceedings of the Fifth Congress of the European Society for Research in Mathematics Education (pp. 288–297). Larnaca, Cyprus: University of Cyprus.
McLeod, D. B. (1992). Research on affect in mathematics education: A reconceptualization. In D. A. Grouws (Ed.), Handbook of research on mathematics, teaching and learning (pp. 575–596). New York: Macmillan.
Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its consequences for young adolescents’ course enrollment intentions and performances in mathematics. Journal of Educational Psychology, 82, 60–70.
Minnaert, A., Boekaerts, M., & Opdenakker, M.-C. (2008). The relationship between students’ interest development and their conceptions and perceptions of group work. Unterrichtswissenschaft, 36(3), 216–236.
Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88, 203–214.
Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user’s guide (5th ed.). Los Angeles, CA: Muthe’n & Muthe’n.
Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316.
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author.
Neubrand, M. (2006). Multiple Lösungswege für Aufgaben: Bedeutung für Fach, Lernen, Unterricht und Leistungserfassung [Multiple solution methods for problems: importance for content, learning, teaching and measurement of performance]. In W. Blum, C. Drüke-Noe, R. Hartung, & O. Köller (Eds.), Bildungsstandards Mathematik: konkret. Sekundarstufe I: Aufgabenbeispiele, Unterrichtsanregungen, Fortbildungsideen [Standards for school mathematics on the low-secondary level: tasks, ideas for teaching and teacher trainigs] (pp. 162–177). Berlin: Cornelsen.
Niss, M., Blum, W., & Galbraith, P. L. (2007). Introduction. In W. Blum, P. L. Galbraith, H.-W. Henn, & M. Niss (Eds.), Modelling and applications in mathematics education: The 14th ICMI study (pp. 1–32). New York: Springer.
OECD (2004). Learning for tomorrow’s world: First results from PISA 2003. Paris, France: OECD.
Panaoura, A., Gagatsis, A., & Demetriou, A. (2009). An intervention to the metacognitive performance: Self-regulation in mathematics and mathematical modeling. Acta Didactica Universitatis Comenianae Mathematics, 9, 63–79.
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48.
Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74(4), 525–556.
Pietsch, J., Walker, R., & Chapman, E. (2003). The relationship among self-concept, self-efficacy, and performance in mathematics during secondary school. Journal of Educational Psychology, 95(3), 589–603.
Pollak, H. (1979). The interaction between mathematics and other school subjects. In H.-G. Steiner & B. Christiansen (Eds.), New trends in mathematics teaching (Vol. IV, pp. 232–248). Paris, France: United Nations Educational Scientific and Cultural Organization.
Rakoczy, K., Harks, B., Klieme, E., Blum, W., & Hochweber, J. (2013). Written feedback in mathematics: Mediated by students’ perception, moderated by goal orientation. Learning and Instruction, 27, 63–73.
Reed, S. K., Stebick, S., Comey, B., & Carroll, D. (2012). Finding similarities and differences in the solutions of word problems. Journal of Educational Psychology, 104, 636–646.
Richland, L. E., Zur, O., & Holyoak, K. J. (2007). Cognitive supports for analogies in the mathematics classroom. Science, 316, 1128–1129.
Rittle-Johnson, B., & Star, J. R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology, 99(3), 561–574.
Rittle-Johnson, B., & Star, J. R. (2009). Compared with what? The effects of different comparisons on conceptual knowledge and procedural flexibility for equation solving. Journal of Educational Psychology, 101(3), 529–544.
Rittle-Johnson, B., Star, J. R., & Durkin, K. (2009). The importance of prior knowledge when comparing examples: Influences on conceptual and procedural knowledge of equation solving. Journal of Educational Psychology, 101(4), 836–852.
Rittle-Johnson, B., Star, J. R., & Durkin, K. (2012). Developing procedural flexibility: Are novices prepared to learn from comparing procedures? British Educational Research Journal, 82(3), 436–455.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.
Ryan, R. M., & Deci, E. L. (2009). Promoting self-determined school engagement: Motivation, learning, and well-being. In K. R. Wentzel & A. Wigfield (Eds.), Handbook on motivation at school (pp. 171–196). New York: Routledge.
Schukajlow, S., Blum, W., Messner, R., Pekrun, R., Leiss, D., & Müller, M. (2009). Unterrichtsformen, erlebte Selbständigkeit, Emotionen und Anstrengung als Prädiktoren von Schüler-Leistungen bei anspruchsvollen mathematischen Modellierungsaufgaben [Teaching methods, perceived self-regulation, emotions, and effort as predictors for students’ performance while solving mathematical modelling tasks]. Unterrichtswissenschaft, 37(2), 164–186.
Schukajlow, S., & Krug, A. (2012). Effects of treating multiple solutions on students’ self-regulation, self-efficacy and value. In T. Y. Tso (Ed.), Proceedings of the 36th Conference of the International Group for the Psychology of Mathematics Education (Vol. 4, pp. 59–66). Taipei, Taiwan: PME.
Schukajlow, S., & Krug, A. (2013a). Considering multiple solutions for modelling problems - design and first results from the MultiMa-Project. In G. Stillman, G. Kaiser, W. Blum, & J. Brown (Eds.), International Perspectives on the Teaching and Learning of Mathematical Modelling (ICTMA 15 Proceedings) (pp. 207–216). Heidelberg: Springer.
Schukajlow, S., & Krug, A. (2013b). Planning, monitoring and multiple solutions while solving modelling problems. In A. M. Lindmeier, & A. Heinze (Eds.), Proceedings of the 37th Conference of the International Group for the Psychology of Mathematics Education (Vol. 4, pp. 177–184). Kiel, Germany: PME.
Schukajlow, S., & Krug, A. (2013c). Uncertainty orientation, preferences for solving tasks with multiple solutions and modelling. In B. Ubuz, Ç. Haser, & M. A. Mariotti (Eds.), Proceedings of the Eight Congress of the European Society for Research in Mathematics Education (pp. 1429–1438). Ankara, Turkey: Middle East Technical University.
Schukajlow, S., & Krug, A. (2014a). Are interest and enjoyment important for students’ performance? In C. Nicol, S. Oesterle, P. Liljedahl, & D. Allan (Eds.), Proceedings of the Joint Meeting of PME 38 and PME-NA 36 (Vol. 5, pp. 129–136). Vancouver, Canada: PME.
Schukajlow, S., & Krug, A. (2014b). Do multiple solutions matter? Prompting multiple solutions, interest, competence, and autonomy. Journal for Research in Mathematics Education, 45(4), 497–533.
Schukajlow, S., Leiss, D., Pekrun, R., Blum, W., Müller, M., & Messner, R. (2012). Teaching methods for modelling problems and students’ task-specific enjoyment, value, interest and self-efficacy expectations. Educational Studies in Mathematics, 79(2), 215–237.
Silver, E. A. (1995). The nature and use of open problems in mathematics education: Mathematical and pedagogical perspectives. ZDM The International Journal on Mathematics Education, 27(2), 67–72.
Silver, E. A., Ghousseini, H., Gosen, D., Charalambous, C. Y., & Font Strawhun, B. T. (2005). Moving from rhetoric to praxis: Issues faced by teachers in having students consider multiple solutions for problems in the mathematics classroom. Journal of Mathematical Behavior, 24(3–4), 287–301.
Slavin, R. E., Hurley, E. A., & Chamberlain, A. (2003). Cooperative learning and achievement: Theory and research. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (Vol. 7, pp. 177–198). New York: Wiley.
Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89(6), 845–851.
Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In The tenth annual conference of the cognitive science society (pp. 375–383). Hillsdale, NJ: Lawrence Erlbaum.
Stacey, K. (1995). The challenges of keeping open problem solving open in school mathematics. ZDM The International Journal on Mathematics Education, 27(2), 62–67.
Star, J. R., & Rittle-Johnson, B. (2008). Flexibility in problem solving: The case of equation solving. Learning and Instruction, 18, 565–579.
Star, J. R., & Rittle-Johnson, B. (2009). It pays to compare: An experimental study on computational estimation. Journal of Experimental Child Psychology, 102(4), 408–426.
Stylianides, A. J., & Stylianides, G. J. (2014). Impacting positively on students’ mathematical problem solving beliefs: An instructional intervention of short duration. Journal of Mathematical Behavior, 33, 8–29.
Tsamir, P., Tirosh, D., Tabach, M., & Levenson, E. (2010). Multiple solution methods and multiple outcomes—is it a task for kindergarten children? Educational Studies in Mathematics, 73(3), 217–231.
Verschaffel, L., Greer, B., & De Corte, E. (2000). Making sense of word problems. Lisse: Swets and Zeitlinger.
Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427–450.
Webb, N. M., & Palincsar, A. S. (1996). Group processes in the classroom. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 841–873). New York: Macmillan.
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81.
Wu, M. L., Adams, R. J., & Wilson, M. R. (1998). ACER conquest. Melbourne: The Australian Council for Educational Research.
Zan, R., Brown, L., Evans, J., & Hannula, M. S. (2006). Affect in mathematics education: An introduction. Educational Studies in Mathematics, 63(2), 113–122.
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Selfregulated learning and academic achievement: Theoretical perspectives. Mahwah, NJ: Erlbaum.
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The research project MultiMa, directed by Stanislaw Schukajlow, has been funded by the German Research Foundation [Deutsche Forschungsgemeinschaft] since 2011.
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Schukajlow, S., Krug, A. & Rakoczy, K. Effects of prompting multiple solutions for modelling problems on students’ performance. Educ Stud Math 89, 393–417 (2015). https://doi.org/10.1007/s10649-015-9608-0
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DOI: https://doi.org/10.1007/s10649-015-9608-0