Can AI be racist? Color‐evasiveness in the application of machine learning to science assessments

Cheuk, Tina

doi:10.1002/sce.21671

Cited by 39 publications

(31 citation statements)

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“…Machine Learning in Science Education Research In the past several years, there has been a coalescence of work carried out in science education on how ML can impact our research and assessment efforts. Central to this coalescing of research have been critical essays (Cheuk, 2021), frameworks (Zhai et al, 2020a), review papers (Zhai et al, 2020b), and meta-analyses (Zhai et al, 2021) on the use of ML in science education contexts. We begin our review of this research with what has been identified as the predominant use: as a part of supervised ML applications.…”

Section: A Primer On Machine Learningmentioning

confidence: 99%

“…Furthermore, as unsupervised methods usually require relatively large amounts of data to identify patterns reliably, patterns from underrepresented groups may be missed. For instance, Cheuk (2021) describes cases in which computers struggle with vernacular expressions. Further, unsupervised approaches such as clustering algorithms only group data based on statistical patterns but do not provide interpretations of what the groups represent.…”

Section: Place Figure 2 Herementioning

confidence: 99%

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Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research

2022

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Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

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Section: A Primer On Machine Learningmentioning

confidence: 99%

Section: Place Figure 2 Herementioning

confidence: 99%

Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research

2022

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“…Equity is also an issue in the context of the learning analytics which are envisioned in the framework and procedure here. Recent research has demonstrated and documented the widespread range of equity issues in the context of machine learning or artificial intelligence methods, i.e., learning analytics techniques, more broadly (O'Neil, 2016;Benjamin, 2019;Cheuk, 2021;Crawford, 2021). While frameworks for addressing equity issues in learning analytics exist (e.g., Floridi et al, 2018), they rarely provide guidance for the concrete issues and highly disciplinary affordances that designers face (see also Kitto and Knight, 2019).…”

Section: Discussionmentioning

confidence: 99%

Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design

et al. 2022

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National educational standards stress the importance of science and mathematics learning for today’s students. However, across disciplines, students frequently struggle to meet learning goals about core concepts like energy. Digital learning environments enhanced with artificial intelligence hold the promise to address this issue by providing individualized instruction and support for students at scale. Scaffolding and feedback, for example, are both most effective when tailored to students’ needs. Providing individualized instruction requires continuous assessment of students’ individual knowledge, abilities, and skills in a way that is meaningful for providing tailored support and planning further instruction. While continuously assessing individual students’ science and mathematics learning is challenging, intelligent tutoring systems show that it is feasible in principle. However, the learning environments in intelligent tutoring systems are typically not compatible with the vision of how effective K-12 science and mathematics learning looks like. This leads to the challenge of designing digital learning environments that allow for both – meaningful science and mathematics learning and the reliable and valid assessment of individual students’ learning. Today, digital devices such as tablets, laptops, or digital measurement systems increasingly enter science and mathematics classrooms. In consequence, students’ learning increasingly produces rich product and process data. Learning Analytics techniques can help to automatically analyze this data in order to obtain insights about individual students’ learning, drawing on general theories of learning and relative to established domain specific models of learning, i.e., learning progressions. We call this approach Learning Progression Analytics (LPA). In this manuscript, building of evidence-centered design (ECD), we develop a framework to guide the development of learning environments that provide meaningful learning activities and data for the automated analysis of individual students’ learning – the basis for LPA and scaling individualized instruction with artificial intelligence.

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“…In the past several years, there has been a coalescence of work carried out in science education on how ML can impact our research and assessment efforts. Central to this coalescing of research have been critical essays (Cheuk, 2021), frameworks (Zhai et al, 2020a), review papers (Zhai et al, 2020b), and meta-analyses (Zhai et al, 2021) on the use of ML in science education contexts. We begin our review of this research with what has been identified as the predominant use: as a part of supervised ML applications.…”

Section: In Science Education Researchmentioning

confidence: 99%

Distributing Epistemic Functions and Tasks - A Framework for Augmenting Human Analytic Power With Machine Learning in Science Education Research

Kubsch¹,

Krist²,

Rosenberg³

2022

Preprint

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Machine learning has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students’ work. Despite this promise, we and other scholars argue that machine learning has not achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers’ use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting-edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human-driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

show abstract

Can AI be racist? Color‐evasiveness in the application of machine learning to science assessments

Cited by 39 publications

References 61 publications

Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research

Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research

Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design

Distributing Epistemic Functions and Tasks - A Framework for Augmenting Human Analytic Power With Machine Learning in Science Education Research

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