The symbol linked explicit unpacking (SLEU) method for solving STEM problems

Adassa Phillips 1, Muizz Hassanali 1, James A. Wingrave 1 *
More Detail
1 Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, 19716, USA
* Corresponding Author
EUR J SCI MATH ED, Volume 7, Issue 4, pp. 156-168.
OPEN ACCESS   1082 Views   639 Downloads
Download Full Text (PDF)


Solving STEM problems requires that data (numbers and units) from a word problem be substituted into a mathematical equation(s) for solution. However, solving STEM problems is an implicit process that provides little or no guidance for the problem solving process. In this study symbols linking the data to the mathematical equation(s) are used to develop an explicit unpacking method for solving STEM problems. This problem solving procedure is termed the SLEU (Symbol Linked Explicit Unpacking) method. The SLEU method provides an explicit stepwise framework to guide students in the solution of STEM problems from the word-problem format of most STEM problems to the mathematical set up and ultimate solution of the problem. Simultaneously, the SLEU method requires students to understand the physicochemical significance of the data in the word problem as it is used in the mathematical equation. The SLEU problem steps can be easily uploaded into software programs for distribution to students for individual, self-guided study.


Phillips, A., Hassanali, M., & Wingrave, J. A. (2019). The symbol linked explicit unpacking (SLEU) method for solving STEM problems. European Journal of Science and Mathematics Education, 7(4), 156-168.


  • Adams, W. K. (2012). Problem solving assessment. The Journal of the Acoustical Society of America, 132(3), 1923.
  • Ball, D. L. & Bass, H. (2000). Interweaving content and pedagogy in teaching and learning to teach: Knowing and using mathematics. In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning, 83-104. Westport, CT: Ablex Publishing.
  • Bauersfeld, H. (1995). The structuring of the structures: Development and function of mathematizing as a social practice. In L. P. Steffe, & J. Gale (Eds.), Constructivism in education, 137-158. Hillsdale, NJ & Hove, UK: Lawrence Erlbaum Associates Publishers.
  • Caine, G. Caine, R. N. & Cromwell, S. (1994). Mindshifts: A brain-based process for restructuring schools and renewing education. Tuscon, AZ: Zephr Press.
  • Doll, W. C. (1989). Complexity in the classroom. Educational Leadership, 7(1). 6570.
  • Lesh, R., & Doerr, H. M. (2003). Foundation of a models and modeling perspective on mathematics teaching, learning, and problem solving. In R. Lesh, & H. M. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching, 3-34. Mahwah and London: Lawrence Erlbaum Associates Publishers.
  • Lester, F. K. (1994). Musings about mathematical problem-solving research: 1970-1994. Journal for Research in Mathematics Education, 25(6), 660-675.
  • Niss, M., Blum, W. & Galbraith, P. (2007). Introduction. In Blum, W., Galbraith,P.L., Henn, H.-W. & Niss, M. (Eds.), Modelling and Applications in Mathematics Education. The 14 th ICMI Study, 3-32. New-York: Springer.
  • Schoenfeld, A. H. (2007). Problem solving in the United States, 1970–2008: research and theory, practice and politics. ZDM Mathematics Education, 39, 537-551.
  • Shulman, L. S., & Keislar, E. R. (1966). Learning by discovery. Chicago: Rand McNally.
  • Zhang, Pingping (2014), Unpacking Mathematical Problem Solving through a Concept-Cognition-Metacognition Theoretical Lens, Dissertation, Ohio State University.
  • Zhang, P., Brosnan, P., Erchick, D., & Liu, Y. (2010). Analysis and inference to students' approaches about development of problem-solving ability. In P. Brosnan, D. Erchick, L. Flevares (Eds), Proceedings of the 32nd annual conference of the Psychology of Mathematics Education North American Chapter.