A Learner-Centered Approach to Teaching Computational Modeling, Data Analysis, and Programming

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11540)


One of the core missions of Michigan State University’s new Department of Computational Mathematics, Science, and Engineering is to provide education in computational modeling and data science to MSU’s undergraduate and graduate students. In this paper, we describe our creation of CMSE 201, “Introduction to Computational Modeling and Data Analysis,” which is intended to be a standalone course teaching students core concepts in data analysis, data visualization, and computational modeling. More broadly, we discuss the education-research-based rationale behind the “flipped classroom” instructional model that we have chosen to use in CMSE 201, which has also informed the design of other courses taught in the department. We also explain the course’s design principles and implementation.


Computational science education Data analysis Modeling 



The authors thank Nathan Brugnone, Danny Caballero, Andrew Christlieb, Dirk Colbry, Sarah Gady, Nat Hawkins, Morten Hjorth-Jensen, and Luke Stanek for useful discussion and constructive criticism on drafts of this manuscript. We further thank all instructors of CMSE 201 for their enthusiastic participation and thoughtful feedback during the course creation process. We thank the MSU CMSE Department, the Office of the Vice President for Research and Graduate Studies, the College of Natural Science, the MSU Connected Mathematics Endowment (as administered by the MSU Program in Mathematics Education), and the Howard Hughes Medical Institute for their generous support.


  1. 1.
    Ambrose, S.A., Bridges, M.W., DiPietro, M., Lovett, M.C., Norman, M.K.: How Learning Works: Seven Research-Based Principles for Smart Teaching. Wiley, Hoboken (2010)Google Scholar
  2. 2.
    Black, P., Wiliam, D.: Inside the black box: raising standards through classroom assessment. Phi Delta Kappan 80(2), 139–144 (1998)Google Scholar
  3. 3.
    Caballero, M.D., Hjorth-Jensen, M.: Integrating a computational perspective in physics courses. ArXiv e-prints, February 2018Google Scholar
  4. 4.
    Chapin, H.C., Wiggins, B.L., Martin-Morris, L.E.: Undergraduate science learners show comparable outcomes whether taught by undergraduate or graduate teaching assistants. J. Coll. Sci. Teach. 44(2), 90–99 (2014)CrossRefGoogle Scholar
  5. 5.
    Cummings, K., Marx, J., Thornton, R., Kuhl, D.: Evaluating innovation in studio physics. Am. J. Phys. 67(S1), S38–S44 (1999)CrossRefGoogle Scholar
  6. 6.
    Goertzen, R.M., Brewe, E., Kramer, L.H., Wells, L., Jones, D.: Moving toward change: institutionalizing reform through implementation of the learning assistant model and open source tutorials. Phys. Rev. Spec. Top. Phys. Educ. Res. 7(2), 020105 (2011)CrossRefGoogle Scholar
  7. 7.
    Gray, J., Chambers, L., Bounegru, L.: The Data Journalism Handbook: How Journalists Can Use Data to Improve the News. O’Reilly Media Inc., Sebastopol (2012)Google Scholar
  8. 8.
    Henke, N., et al.: The age of analytics: competing in a data-driven world. McKinsey & Company (2016).
  9. 9.
    Irving, P.W., Obsniuk, M.J., Caballero, M.D.: P3: a practice focused learning environment. Eur. J. Phys. 38(5), 055701 (2017)CrossRefGoogle Scholar
  10. 10.
    Kohl, P.B., Vincent Kuo, H.: Chronicling a successful secondary implementation of studio physics. Am. J. Phys. 80(9), 832–839 (2012)CrossRefGoogle Scholar
  11. 11.
    National Academy of Sciences, Committee on Trends and Opportunities in Federal Earth Science Education and Workforce Development, Board on Earth Sciences and Resources, Division on Earth and Life Studies: Preparing the Next Generation of Earth Scientists: An Examination of Federal Education and Training Programs. National Academies Press.
  12. 12.
    National Association of Colleges and Employers: Job Outlook 2016: The Attributes Employers Want to See on New College Graduates’ Resumes.
  13. 13.
    National Association of Colleges and Employers: Job Outlook 2017.
  14. 14.
    O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books (2016)Google Scholar
  15. 15.
    Otero, V., Pollock, S., Finkelstein, N.: A physics department’s role in preparing physics teachers: the Colorado learning assistant model. Am. J. Phys. 78(11), 1218–1224 (2010)CrossRefGoogle Scholar
  16. 16.
    Petter Sand, O., Odden, T.O.B., Lindstrøm, C., Caballero, M.D.: How computation can facilitate sensemaking about physics: a case study. ArXiv e-prints, July 2018Google Scholar
  17. 17.
    Qian, Y., Lehman, J.: Students’ misconceptions and other difficulties in introductory programming: a literature review. ACM Trans. Comput. Educ. 18(1), 1:1–1:24 (2017)CrossRefGoogle Scholar
  18. 18.
    Wiggins, G.P., McTighe, J.: Understanding by Design. Association for Supervision and Curriculum Development, expanded 2nd edn.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computational Mathematics, Science, and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Fullstack AcademyNew YorkUSA

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