Computer-Based Learning in Context: CFP, Special Issue on The Long Tail of Algorithmic Bias in Education: Intersectionality and Less-Studied Categories of Identity

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Ryan Baker

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Nov 18, 2021, 4:09:40 PM11/18/21
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Computer-Based Learning in Context

Special Issue on The Long Tail of Algorithmic Bias in Education:
Intersectionality and Less-Studied Categories of Identity

Call For Papers


The last couple of years have seen an explosion of interest in
algorithmic bias in education, matching greater societal awareness of
the problem of algorithmic bias in general, and problems of
discrimination and social justice more broadly.

However, most of the work on algorithmic bias in education (and
algorithmic bias in general) has focused on easily identified and
well-known demographic categories. A recent review by Baker and Hawn
(2021) finds that in the relatively rarer cases when researchers have
looked for algorithmic bias in terms of other categories, they often
find evidence for its existence. This suggests that other unknown
categories may be impacting algorithmic effectiveness. Furthermore,
algorithmic bias often is posed in terms of single categories,
ignoring the possibility that bias may emerge at the intersection of
categories as well.

This special issue seeks to promote research and practice that
investigates and attempts to resolve less-studied algorithmic biases
in education. Work on biases going beyond widely-studied demographic
categories is welcome; this includes work that spans both widely
-studied and non-widely-studied categories. Work on intersectional
biases is also welcome. We welcome theoretical papers, conceptual and
position papers, empirical papers, methodological papers, and papers
of practice.

Sample topics may include:

·         Empirical research on whether algorithmic bias investigating
less-studied categories is present in a specific application

·    Including but not limited to work involving indigenous
populations, sub-categories of widely-studied demographic categories,
learners with specific disabilities, neurodiversity,
military-connected children, migrant workers and their families,
non-binary and transgender learners, religious minorities, refugees,
rural learners, learners in small or remote cities or communities,
non-WEIRD countries, speakers of less common dialects or non-prestige
dialects, second-language speakers, and international students or
students of specific national backgrounds

·         Empirical research on intersectional algorithmic biases

·         Empirical work to address and resolve less-studied
algorithmic bias and intersectional algorithmic biases

·         Mathematical work and methodology related to studying
less-studied and/or intersectional algorithmic biases, including but
not limited to power analyses and  sample size calculations

·         Conceptual, theoretical, and position pieces related to
journal special issue themes

·         Work around data systems and methods that enable research on
less-studied groups

·         Case studies around efforts to reduce algorithmic bias (of
the type this special issue focuses on) in practice



Submission and Inquiries

Please see https://www.upenn.edu/learninganalytics/CBLC/submission.html
for submission information. We welcome manuscripts of any length and
welcome dual-publication both in English and other languages.

When you submit your paper, please note that it is for this special
issue in your cover letter

All submissions will go through the journal’s usual peer review process.



Important Dates:

·         Email inquiry of interest for submitting to special issues
or abstract: any date before August 1, 2022 (optional)

·         Paper submission: September 1, 2022

·         All articles will be published online as soon as fully
accepted, and as part of a special issue when all submissions have
completed their processes



Guest editors:

·         Nigel Bosch, University of Illinois Urbana-Champaign, p...@illinois.edu
·         Ibrahim Dahlstrom-Hakki, TERC, idahlst...@terc.edu
·         Ryan S. Baker, University of Pennsylvania, ryb...@upenn.edu

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