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The Case for Acknowledging Subjectivity in CS Education Research Data

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Jayce R. WarnerAuthors Info & Claims

SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2

Pages 1013 - 1014

https://doi.org/10.1145/3478432.3499225

Published: 03 March 2022 Publication History

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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.

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[2]

Darrell Huff. 1954. How to Lie with Statistics .W.W. Norton & Company.

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[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.

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Kumar PSchulman JAlbargi FBhattacharyya SDong HLiu ZStone JYuen TShoop LRebelsky SPrather J(2025)Ungrading as a Pedagogy for Teaching Qualitative Research Methods in ComputingProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701814(631-637)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701814

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The Case for Acknowledging Subjectivity in CS Education Research Data

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Published In

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

General Chairs:
Larry Merkle
Air Force Institute of Technology, USA
,
Maureen Doyle
Northern Kentucky University, USA
,
Program Chairs:
Judithe Sheard
Monash University, Australia
,
Leen-Kiat Soh
University of Nebraska-Lincoln, USA
,
Brian Dorn
University of Nebraska at Omaha, USA

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.

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Association for Computing Machinery

New York, NY, United States

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Published: 03 March 2022

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SIGCSE

SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education

March 3 - 5, 2022

RI, Providence, USA

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Kumar PSchulman JAlbargi FBhattacharyya SDong HLiu ZStone JYuen TShoop LRebelsky SPrather J(2025)Ungrading as a Pedagogy for Teaching Qualitative Research Methods in ComputingProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701814(631-637)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701814

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Recommendations

CS Education: Catching the Wave

Graduate Programs in CS Education: Why 2020 is the Right Time

Undergraduate Computational Science and Engineering Education

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