skip to main content
10.1145/3478432.3499225acmconferencesArticle/Chapter ViewAbstractPublication PagessigcseConference Proceedingsconference-collections
panel

The Case for Acknowledging Subjectivity in CS Education Research Data

Published:03 March 2022Publication History

ABSTRACT

Is quantitative data collected by CS education researchers objective? If we combine data from a set of studies that measure the same type of intervention, will that really show us the strength of that intervention? Are qualitative studies really less rigorous than quantitative because the number of participants may be as low as one?

In this panel, we will first present the different types of studies that are most common in CS education research and provide a working definition for what we mean by various types of research methodologies (e.g., quantitative, qualitative, mixed methods). Drawing upon our experiences in the field of studying computing education, we will then explore some of the myths surrounding data, highlighting where evidence presented through research data is rigorous and, when not, how we (as researchers) can mitigate the risks of collecting and sharing data that is unsound in publications.

We encourage you to attend. After all, this panel has been recommended by four out of five computer science education researchers.

References

  1. Sanne F Akkerman, Arthur Bakker, and William R Penuel. 2021. Relevance of Educational Research: An Ontological Conceptualization. Educational Researcher (2021), 0013189X211028239.Google ScholarGoogle Scholar
  2. Darrell Huff. 1954. How to Lie with Statistics .W.W. Norton & Company.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Monica M McGill, Tom McKlin, and Errol Kaylor. 2019. Defining what empirically works best: Dynamic generation of meta-analysis for computer science education. In Proceedings of the 2019 ACM Conference on ICER . 199--207.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The Case for Acknowledging Subjectivity in CS Education Research Data

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2
          March 2022
          254 pages
          ISBN:9781450390712
          DOI:10.1145/3478432

          Copyright © 2022 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 3 March 2022

          Check for updates

          Qualifiers

          • panel

          Acceptance Rates

          Overall Acceptance Rate1,595of4,542submissions,35%

          Upcoming Conference

          SIGCSE Virtual 2024
          SIGCSE Virtual 2024: ACM Virtual Global Computing Education Conference
          November 30 - December 1, 2024
          Virtual Event , USA
        • Article Metrics

          • Downloads (Last 12 months)31
          • Downloads (Last 6 weeks)4

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader