Abstract
Mathematical modelling is a popular tool used to solve many quantitative business problems. It is a challenge to teach mathematical modelling skills to students, in non-mathematics majors, from business schools for example, who will potentially be employed to tackle business problems raised in market competition. In discussing the method to estimate customer lifetime value (CLV), as an example, we focus on the dynamical relationship among variables rather than simply setting up a formula from which the subject can be readily solved. Such a dynamical system approach exhibits the logistic nature of the CLV model. The pedagogical implication of learning this logistic property is that the learnt technique is applicable in various market scenarios. In showing this model, it is asserted that mathematical modelling is not merely a variation of problem solving.
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References
Ball, D. L., Sleep, L., Boerst, T. A., & Bass, H. (2009). Combining the development of practice and the practice of development in teacher education. The Elementary School Journal, 109(5), 458–474.
Belfort, E., & Guimaraes, L. C. (2002). The influence of subject matter on teachers’ practices: Two case studies. In A. D. Cockburn & E. Nardi (Eds.), Proceedings of the 26th conference of the International Group for the Psychology of Mathematics Education (Vol. 1, pp. 73–80). Norwich.
Berger, P. D., & Nasr, N. (1998). Customer lifetime value: Marketing models and application. Journal of Interactive Marketing, 12(1), 17–30.
Doerr, H. M., & English, L. D. (2003). A modelling perspective on students’ mathematical reasoning about data. Journal for Research in Mathematics Education, 34(2), 110–136.
Dwyer, F. R. (1989). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 8(2), 73–81.
Gravemeijer, K. P. E. (1994). Developing realistic mathematics education. Utrecht: Freudenthal Institute.
Hill, H. C., Ball, D. L., & Schilling, S. G. (2008). Unpacking pedagogical content knowledge: Conceptualizing and measuring teachers’ topic-specific knowledge of students. Journal for Research in Mathematics Education, 39(4), 372–400.
Klein, F. (2004a). Elementary mathematics from an advanced standpoint: Vol. 1. Algebra & analysis. (E. R. Hedrick & C. A. Noble, Trans.). New York: Dover Publications. (Original work published 1908)
Klein, F. (2004b). Elementary mathematics from an advanced standpoint: Vol. 2: Geometry. (E. R. Hedrick & C. A. Noble, Trans.). New York: Dover Publications. (Original work published 1908)
Kotler, P., & Armstrong, G. (1996). Principals of marketing (7th ed.). Englewood Cliffs: Prentice-Hall.
Lesh, R., & Zawojewski, J. (2007). Problem solving and modelling. In F. Lester (Ed.), Handbook of research on mathematics teaching and learning (2nd ed., pp. 763–804). Reston/Charlotte: NCTM/Information Age Publishing.
Leung, H. Y., Ching, W. K., & Leung, I. K. C. (2008). A stochastic optimization model for consecutive promotion. Quality Technology and Quantitative Management, 5(4), 403–414.
Li, J., Fan, X., & Zhu, Y. (2010). A framework on mathematical knowledge for teaching. In Proceedings of the 5th East Asia Regional Conference on Mathematics Education (EARCOME 5), (Vol. 2, pp. 1–8). Tokyo.
Newby, T. J., Stepich, D., Lehman, J., & Russell, J. (2006). Educational technology for teaching and learning (3rd ed.). Upper Saddle River: Merrill/Prentice Hall.
Reichheld, F. (1996). The loyalty effect. Boston: Harvard Business School Press.
Viadero, D. (2004). Teaching mathematics requires special set of skills. Education Week, 24(7), 8.
Sweller, J., Clark, R., & Kirschner, P. (2010). Teaching general problem-solving skills is not a substitute for, or a viable addition to, teaching mathematics. Notices of the American Mathematical Society, 57(10), 1303–1304.
Zawojewski, J. (2007). Problem solving versus modeling. In R. Lesh, P. Galbraith, C. R. Haines, & A. Hurford (Eds.), Modeling students’ mathematical modelling competencies (pp. 237–243). New York: Springer.
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Leung, I.K.C. (2013). Beyond the Modelling Process: An Example to Study the Logistic Model of Customer Lifetime Value in Business Marketing. In: Stillman, G., Kaiser, G., Blum, W., Brown, J. (eds) Teaching Mathematical Modelling: Connecting to Research and Practice. International Perspectives on the Teaching and Learning of Mathematical Modelling. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6540-5_52
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DOI: https://doi.org/10.1007/978-94-007-6540-5_52
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