Using Online Practice Spaces to Investigate Challenges in Enacting Principles of Equitable Computer Science Teaching

Robinson, Kevin L.; Jahanian, Keyarash; Reich, Justin

doi:10.1145/3159450.3159503

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Although equity simulations are a potentially promising practice in DEI education (Robinson et al, 2018;Self, 2016), their utility as a teaching tool is somewhat constrained by the challenge of quickly interpreting and providing feedback on open-text responses. In this study, we explored the potential for using unsupervised NLP techniques to detect patterns within participants' responses to prompts within equity teaching simulations.…”

Section: Discussionmentioning

confidence: 99%

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Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

2021

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Diversity, equity, and inclusion (DEI) issues are urgent in education. We developed and evaluated a massive open online course ( N = 963) with embedded equity simulations that attempted to equip educators with equity teaching practices. Applying a structural topic model (STM)—a type of natural language processing (NLP)—we examined how participants with different equity attitudes responded in simulations. Over a sequence of four simulations, the simulation behavior of participants with less equitable beliefs converged to be more similar with the simulated behavior of participants with more equitable beliefs ( ES [effect size] = 1.08 SD). This finding was corroborated by overall changes in equity mindsets ( ES = 0.88 SD) and changed in self-reported equity-promoting practices ( ES = 0.32 SD). Digital simulations when combined with NLP offer a compelling approach to both teaching about DEI topics and formatively assessing learner behavior in large-scale learning environments.

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Section: Discussionmentioning

confidence: 99%

“…Accordingly, most studies of simulation have either limited their analysis to a small number of participants (G. A. Chen, 2020;Robinson et al, 2018;Self, 2016) or have focused on participants' reactions to the simulations rather than behavior in the simulations themselves (Bautista & Boone, 2015;J. A. Chen et al, 2020;Dalinger et al, 2020;Girod & Girod, 2006).…”

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confidence: 99%

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

2021

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“…Although equity simulations are a potentially promising practice in DEI education (Borneman et al, 2020;Robinson et al, 2018;Self, 2016) their utility as a teaching tool is somewhat constrained by the challenge of quickly interpreting and providing feedback on opentext responses. In this study, we examined the results of a 14-week online DEI course for educators that incorporated equity simulations as a means for enacting equity-promoting behavior modeled within the course.…”

Section: Discussionmentioning

confidence: 99%

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

Littenberg-Tobias¹,

Reich²,

Borneman³

2020

Preprint

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Diversity, equity, and inclusion (DEI) issues are urgent in education, given the widespread evidence of discriminatory behavior and widening racial disparities. Although DEI trainings can change participants’ attitudes they have minimal effects on behaviors. Simulations are a promising approach to address this gap between attitudinal and behavioral change. We developed an online course for educators (N = 963) that included a series of equity simulations and applied the structural topic model (STM), to identify alignment between participants' simulated behavior and equity attitudes on surveys. STM identified meaningful topics within participants’ simulation responses and that the prevalence of these topics varied by equity attitudes. We also measured changes in participants' behaviors over different, successive, simulations, by comparing with a reference group of high equity-oriented participants. Participants made significant shifts in the simulations toward equity-promoting behaviors (ES = 0.99), which corresponded with changes in equity-oriented attitudes (ES=0.74) and self-reported equity-promoting behaviors (ES=0.30).

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“…However, bias might also be inferred through teachers' behaviors in the classroom. Robinson et al ( 2018 ) describe a framework for equitable teaching practices, which outlines behaviors indicative of whether a teacher is acting in an unbiased manner in the classroom. These behaviors include positioning students with an asset framing, disrupting preparatory privilege, and honoring overlapping oppression in intersectional identities.…”

Section: Introductionmentioning

confidence: 99%

“…Oppression within such identities—and students' concerns about whether this is happening in the classroom—should be acknowledged by teachers as legitimate and significant and not dismissed. Teachers might help address this oppression by connecting students with role models and actively promoting participation from underrepresented groups in, e.g., computer science (Robinson et al, 2018 ) and AI (Jagannathan and Komives, 2019 ), which are domains typically dominated by white males.…”

Section: Introductionmentioning

confidence: 99%

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Gauthier

Rizvi

Cukurova

et al. 2022

Front. Artif. Intell.

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Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.

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Using Online Practice Spaces to Investigate Challenges in Enacting Principles of Equitable Computer Science Teaching

Cited by 13 publications

References 13 publications

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

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