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4 - A Study Design Process

from Part II - Foundations

Published online by Cambridge University Press:  15 February 2019

By
Amy J. Ko  and
Sally A. Fincher
Edited by
Sally A. Fincher  and
Anthony V. Robins
Sally A. Fincher
Affiliation:
University of Kent, Canterbury
Anthony V. Robins
Affiliation:
University of Otago, New Zealand
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Summary

Designing empirical studies can seem like black magic—powerful but inexplicable. However, far from being magic, study design is like other kinds of design, involving iterative ideation, prototyping, critique, at both large and small scales, eventually converging toward a design that can successfully answer a research question. This chapter discusses the arc of this design process, beginning with strategies for identifying research questions through theory, literature review, and experience; iterative approaches to evaluating a research question’s soundness, importance, and novelty; brainstorming methods for answering a question that are feasible, valid, and ethical; and forms for describing study designs that ensure a coherent thread of argumentation from the first sentence of an introduction through a plan for analyzing data. Throughout, the chapter gives pointers on the specific challenges of designing studies of learning and teaching about computing.
Type
Chapter
Information
The Cambridge Handbook of Computing Education Research , pp. 81 - 101
Publisher: Cambridge University Press
Print publication year: 2019

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References

Babbie, E. R. (2013). The Basics of Social Research, 13th edn. Boston, MA: Cengage Learning.Google Scholar
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report, 13(4), 544–559.Google Scholar
BBC (2017). Election 2017: Methodology. Retrieved from www.bbc.co.uk/news/election-2017-40104373Google Scholar
Bordens, K. S., & Abbott, B. B. (2002). Research Design and Methods: A Process Approach. New York: McGraw-Hill.Google Scholar
Coolican, H. (2017). Research Methods and Statistics in Psychology. Hove, UK: Psychology Press.Google Scholar
Creswell, J. W., & Clark, V. L. P. (2007). Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Sage Publications.Google Scholar
Curtice, J., Fisher, S., Kuha, J., & Mellon, J. (2017). On the 2017 exit poll – Another surprise, another success. Discover Society, Focus, Issue 46. Retrieved from https://discoversociety.org/2017/07/05/focus-on-the-2017-exit-poll-another-surprise-another-success/Google Scholar
Danielsiek, H., Toma, L., & Vahrenhold, J. (2017). An instrument to assess self-efficacy in introductory algorithms courses. In ACM International Computing Education Research Conference (pp. 217–225). New York: ACM.Google Scholar
Fincher, S., Lister, R., Clear, T., Robins, A., Tenenberg, J., & Petre, M. (2005). Multi-institutional, multi-national studies in CSEd research: Some design considerations and trade-offs. In Proceedings of the First International Conference on Computing Education Research (pp. 111–121). New York: ACM.Google Scholar
Jadud, M. C. (2006). An Exploration of Novice Compilation Behaviour in BlueJ (doctoral dissertation). University of Kent.Google Scholar
Lawson, B. (2006). How Designers Think: The Design Process Demystified. New York: Routledge.CrossRefGoogle Scholar
Lee, M. J., Bahmani, F., Kwan, I., LaFerte, J., Charters, P., Horvath, A., Luor, F., Cao, J., Law, C., Beswetherick, M., Long, S., Burnett, M. M., & Ko, A. J. (2014). Principles of a debugging-first puzzle game for computing education. In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 57–64). New York: IEEE.Google Scholar
Lehrer, J. (2010). How We Decide. Boston, MA: Houghton Mifflin Harcourt.Google Scholar
Lishinski, A., Yadav, A., Good, J., & Enbody, R. J. (2016). Learning to program: Gender differences and interactive effects of students’ motivation, goals, and self-efficacy on performance. In ACM International Computing Education Research Conference (pp. 211–220). New York: ACM.Google Scholar
McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B., Laxer, C., Thomas, L., Utting, I., & Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. In ITiCSE Working Group Reports (pp. 125–180). New York: ACM.Google Scholar
McMillan, J. H., & Schumacher, S. (2014). Research in Education: Evidence-based Inquiry, 7th edn. London, UK: Pearson Higher Education.Google Scholar
Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50, 741–749.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1957). Empirical explorations with the logic theory machine. In Proceedings of the Western Joint Computer Conference, Vol. 15 (pp. 218–239). New York: ACM.Google Scholar
Parker, M. C., Guzdial, M., & Engleman, S. (2016). Replication, validation, and use of a language independent CS1 knowledge assessment. In ACM International Computing Education Research Conference (pp. 93–101). New York: ACM.Google Scholar
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory (Vol. 1). Berlin, Germany: Springer.Google Scholar
Tew, A. E., & Guzdial, M. (2011). The FCS1: A language independent assessment of CS1 knowledge. In ACM Technical Symposium on Computer Science Education (pp. 111–116). New York: ACM.Google Scholar
Utting, I., Tew, A. E., McCracken, M., Thomas, L., Bouvier, D., Frye, R., Paterson, J., Caspersen, M. Kolikant, Y. B.-D., Sorva, J., & Wilusz, T. (2013). A fresh look at novice programmers’ performance and their teachers’ expectations. In ITiCSE Working Group Reports (pp. 15–32). New York: ACM.Google Scholar

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