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Educators as Clinicians: Small Data for Education Research

  • Thomas E. LombardiEmail author
  • Amanda M. Holland-Minkley
Chapter
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Abstract

Much work in computing education research focuses on large-scale data collection and analyses, bringing “big data” approaches to bear on the educational research agenda. Drawing on lessons from the medical research community, we argue that the work of many computing education researchers is more akin to that of a medical clinician than an experimental researcher. Education researchers working in a small-class setting will often not be able to exercise the experimental controls necessary for large-scale, statistically-driven research. In this setting, educational researchers must work through the ambiguity and complexity of their classes to respond to the specific needs of their students in much the same way that clinicians respond to the specific needs of their patients. Small-data approaches tailored specifically to such environments can help educators measure their effectiveness when controlled experiments are not an option. As such, we describe a model for “small data” approaches in computing education research and demonstrate a case study where such an approach has been used effectively to analyze curricular changes.

Keywords

Decision trees Small data Big data Computing education research 

References

  1. Al-Zubidy, A., Carver, J. C., Heckman, S., & Sherriff, M. (2016). A (Updated) review of empiricism at the SIGCSE technical symposium. Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 120–125). Memphis, Tennessee, USA: ACM.Google Scholar
  2. Bacchetti, P., Deeks, S. G., & McCune, J. M. (2011). Breaking free of sample size dogma to perform innovative translational research. Science Translational Medicine, 3(87).Google Scholar
  3. Berglund, A., Daniels, M., & Pears, A. (2006). Qualitative research projects in computing education research: An overview. In Proceedings of the 8th Australasian Computing Education Conference. Hobart, Tasmania, Australia.Google Scholar
  4. boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679.CrossRefGoogle Scholar
  5. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Boca Raton, London, New York and Washington D.C.: Chapman & Hall/CRC.Google Scholar
  6. Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26, 1–24.CrossRefGoogle Scholar
  7. Daniels, M., & Pears, A. (2012). Models and methods for computing education research. In Proceedings of the Fourteenth Australasian Computing Education Conference (vol. 123 pp. 95–102). Melbourne, Australia: Australian Computer Society Inc.Google Scholar
  8. Dugard, P., File, P., & Todman, J. (2012). Single-case and small-n experimental designs (second). New York and London: Routledge.Google Scholar
  9. Fincher, S., Tenenberg, J., & Robins, A. (2011). Research design: Necessary bricolage. In Proceedings of the seventh international workshop on Computing education research (pp. 27–32). Providence, Rhode Island, USA: ACM.Google Scholar
  10. Fitzgerald, S., McCauley, R., & Clark, V. L. P. (2011). Report on qualitative research methods workshop. In Proceedings of the 42nd ACM technical symposium on computer science education (pp. 241–242). Dallas, TX, USA: ACM.Google Scholar
  11. Flach, P. (2012). Machine learning: The art and science of algorithms that make sense of data. Cambridge University Press.Google Scholar
  12. Guzdial, M. (2016). Lerner-centered design of computing education: Research on computing for everyone. Morgan & Claypool.Google Scholar
  13. Hazzan, O., Dubinsky, Y., Eidelman, L., Sakhnini, V., & Teif, M. (2006). Qualitative research in computer science education. In Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (pp. 408–412). Houston, Texas, USA: ACM.CrossRefGoogle Scholar
  14. Holland-Minkley, A. M., & Lombardi, T. (2016). Improving Engagement in Introductory Courses with Homework Resubmission. Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 534–539). Memphis, Tennessee, USA: ACM.Google Scholar
  15. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. Incorporated: Springer Publishing Company.Google Scholar
  16. Kinnunen, P., Meisalo, V., & Malmi, L. (2010). Have we missed something?: Identifying missing types of research in computing education. In Proceedings of the Sixth International Workshop on Computing Education Research (pp. 13–22). New York, NY, USA: ACM. doi: 10.1145/1839594.1839598
  17. Pechenizkiy, M., Calders, T., Vasilyeva, E., & De Bra, P. (2008). Mining student assessment data: Lessons drawn from a small scale case study. In R. S. J. d. Baker, T. Barnes, & J. E. Beck (Eds.), Educational Data Mining 2008: 1‎st International Conference on Educational Data Mining, ‎Proceedings. (pp. 187–191). Montreal, Quebec, Canada. Retrieved from http://www.educationaldatamining.org/EDM2008/index.php?page=proceedings
  18. Randolph, J. J. (2007). Computer science education research at the crossroads: A methodological review of computer science education research, 20002005 (Ph.D. thesis). Utah State University, Logan, Utah, USA.Google Scholar
  19. R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
  20. Romero, C., Ventura, S., Espejo, P. G., & Hervas, C. (2008). Data mining algorithms to classify students. In R. S. J. d. Baker, T. Barnes, & J. E. Beck (Eds.), Educational Data Mining 2008: 1‎st International Conference on Educational Data Mining, ‎Proceedings. (pp. 8–17). Montreal, Quebec, Canada. Retrieved from http://www.educationaldatamining.org/EDM2008/index.php?page=proceedings
  21. Savery, J. (2006). Overview of problem-based learning: Definitions and distinctions. The Interdisciplinary Journal of Problem-Based Learning, 1(2), 9–20.Google Scholar
  22. Therneau, T., Atkinson, B., & Ripley, B. (2015). rpart: Recursive partitioning and regression trees. Retrieved from https://CRAN.R-project.org/package=rpart
  23. Weisberg, H. I. (2014). Willful ignorance: The mismeasure of uncertainty. Wiley.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of the Virgin IslandsSt. ThomasUSA
  2. 2.Department of Computing and Information StudiesWashington & Jefferson CollegeWashingtonUSA

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