Computer Modeling and Programming in Algebra

Arnulfo Perez, Kathy Malone, Siva Meenakshi Renganathan, Kimberly Groshong


This paper introduces a novel approach to providing high school students with access to computer science experiences as part of an Algebra unit on linear functions. The approach is being developed and tested as part of a funded National Science Foundation study. The unit piloted in the study integrates computational thinking and computer modeling into a project-based Algebra unit on linear functions. Literature on computational thinking, access to computer science in secondary settings, modeling approaches, project-based learning, and design-based research is described to provide a rationale for the study design. The ultimate goal of the study is to develop a paradigm for integrating computer science experiences into algebra as a way to increase engagement in STEM and computing among students from all backgrounds.


  1. Bannan, B. (2007). The integrative learning design framework: An illustrated example from the domain of instructional technology. In T. Plomp and N. Nieveen (Eds.), An introduction to educational design research (pp. 53-73). Netherlands Institute for Curriculum Development.
  2. Barr, D., Harrison, J., and Conery, L. (2011). Computational Thinking: A Digital Age Skill for Everyone. Learning and Leading with Technology, 38(6), 20-23.
  3. Bickmore-Brand, J. (1993). Implications from recent research in language arts for mathematical teaching. In: Bickmore-Brand, J. (Eds.), Language in mathematics (pp. 1-9). Portsmouth, NH: Heinemann.
  4. Blum, W. (1996). Applications in mathematics instruction - trends and perspectives. In G. Kadunz et al. (Eds.). Trends and prospects, series of mathematics education, volume 23 (pp. 15-38). Vienna: HölderPichler- Tempsky.
  5. Borba, M. C., and Villarreal, M. E. (2006). Humans-withmedia and the reorganization of mathematical thinking: Information and communication technologies, modeling, visualization and experimentation (Vol. 39). Springer Science and Business Media.
  6. Bybee, R. W. (1997). Achieving scientific literacy: From purposes to practices. Portsmouth, NH: Heinemann.
  7. Cobb, P. (2001). Supporting the improvement of learning and teaching in social and institutional context. In S. M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 455- 478). Mahwah, NJ: Erlbaum.
  8. Cobb, P., and Gravemeijer, K. (2008). Experimenting to support and understand learning processes. In A. E. Kelly, R. A. Lesh, and J. Y. Baek (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 68-95). New York, NY: Routledge.
  9. DBRC (The Design Based Research Collective) (2003). Design-Based Research: An Emerging Paradigm for Educational Inquiry. Educational Researcher, 32(1), 5-8.
  10. Gray, M. A. (2008). SAGE: A new mathematics software system. Computing in Science and Engineering, 10(6), 72-75.
  11. Grover, S., and Pea, R. (2013). Computational Thinking in K-12 A Review of the State of the Field. Educational Researcher, 42(1), 38-43.
  12. Gupta, D., Jasper, B., Fuentes S. Q., Cooper, S., Mallam, W. A. (2015). Advancing the field: Development and validation of algebra teachers' self-efficacy instrument. Paper presentation for School Science and Mathematics Association National Conference, Oklahoma City, OK.
  13. Lester, F. K. (1983). Trends and issues in mathematical problem-solving research. In R. Lesh and M. Landau (Eds.), Acquisition of mathematics concepts and processes (pp. 345-395). New York: Academic Press.
  14. Molina, M., Castro, E., and Castro, E. (2007). Teaching experiments within design research. The International Journal of Interdisciplinary Social Sciences, 2(4), 435-440.
  15. National Research Council. (2011). Committee for the workshops on computational thinking: Report of a workshop of pedagogical aspects of computational thinking. Washington, DC: National Academies Press.
  16. Organisation for Economic Co-operation and Development (2012). Country Note: United States. Retrieved from PISA-2012-results-US.pdf.
  17. Schoenfeld, A. H. (1983). Beyond the purely cognitive: belief systems, social cognitions, and metacognitions as driving forces in intellectual performance. Cognitive Science, 7(4), 329-363.
  18. Schmidt, B. (2011). Modelling in the classroom: Obstacles from the teacher's perspective. In G. Kaiser, W. Blum, R. B. Ferri, and G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling: ICMTA 14 (pp. 641-651). New York, NY: Springer.
  19. Schwarz, C. V., and Gwekwerere, Y. N. (2007). Using a guided inquiry and modeling instructional framework (EIMA) to support preservice K8 science teaching. Science Education, 91(1), 158-186.
  20. Spielhagen, F. R. (2006). Closing the achievement gap in math: The long-term effects of eighth-grade Algebra. Journal Of Advanced Academics, 18(1), 34-59.
  21. Stein, W. (2008). SAGE: Open Source Mathematical Software.
  22. Sullivan, D. (1997). Essentials of electrical diagnostics. Retrieved from
  23. Zelle, J. M. (2010). Python programming: an introduction to computer science (2nd ed.). Franklin, Beedle and Associates, Inc.
  24. Zbiek, R. M., and Conner, A. (2006). Beyond motivation: Exploring mathematical modeling as a context for deepening students' understandings of curricular mathematics. Educational Studies in Mathematics, 63(1), 89-112.
  25. Weintrop, D., Beheshti, E., Horn, M. S., Orton, K., Trouille, L., Jona, K., and Wilensky, U. (2014). Interactive Assessment Tools for Computational Thinking in High School STEM Classrooms. In D.
  26. Reidsma, I. Choi, and R. Bargar (Eds.), Proceedings of Intelligent Technologies for Interactive Entertainment: 6th International Conference, INTETAIN 2014, Chicago, (pp. 22-25). Springer International Publishing.

Paper Citation

in Harvard Style

Perez A., Malone K., Renganathan S. and Groshong K. (2016). Computer Modeling and Programming in Algebra . In Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-179-3, pages 281-286. DOI: 10.5220/0005907102810286

in Bibtex Style

author={Arnulfo Perez and Kathy Malone and Siva Meenakshi Renganathan and Kimberly Groshong},
title={Computer Modeling and Programming in Algebra},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Computer Modeling and Programming in Algebra
SN - 978-989-758-179-3
AU - Perez A.
AU - Malone K.
AU - Renganathan S.
AU - Groshong K.
PY - 2016
SP - 281
EP - 286
DO - 10.5220/0005907102810286