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In this paper, we describe and reflect upon the development of critical consciousness and workplace democracy within an experimental workplace called DataWorks. Through DataWorks, we hire adults from communities historically minoritized in computing education and data careers, and train them in entry-level data skills developed through work on client projects. In this process, workers gain a range of skills. Some of these skills are technical, such as programming for data analysis; some are managerial, such as scoping and bidding projects; others are social, perhaps even political, such as the ability to say No to projects. In what follows, we describe a workshop series developed to build the workers' critical literacy and consciousness about their data work, specifically regarding the use of data in machine learning systems. After that, we describe a data project the workers questioned and resisted because they determined the work to be harmful. In that process, they demonstrated and enacted a critical consciousness towards data and machine learning. Reflecting on this enactment of data-focused critical consciousness, we identify themes that characterize a democratic workplace, describe the work of designing for organizational action and institutional relations, and discuss how worker and researcher positionality affects this work. In doing so, we argue for enabling workers to resist and refuse harmful data work and challenge the standard power structures of academic research and data work. 

While much computing education research focuses on formal K-12 and undergraduate CS education, a growing body of work is exploring alternative pathways to computing careers [7, 16], alternative outcomes for computing education [15], and adult learning in workplace communities [9, 13]. Within this context, we are studying novice-friendly computational work as a pathway to computing careers. Novice-friendly computational work is a phrase we use to describe computing activities that have a low barrier to entry, are used in authentic contexts outside formal CS spaces, and are legitimate computational activities, e.g., data work [13], web design [5], and Salesforce CRM [9]. Learning through authentic work practices is a promising pathway to computing careers because it poses lower financial and findability barriers than coding bootcamps [14] and online courses [4]. However, gatekeeping culture in computing deems novice-friendly tools like Excel, HTML/CSS, and JSON distinct from “real” programming [12]. Further, novice workers may not be considered legitimate peripheral members of computing communities of practice despite engaging in legitimate computational work [6, 11]. 

Artistic computing learning environments have been of central importance in the exploration of how to support equity and inclusion in computing. Explorations within e-textiles, music, and interactive media, for example, have created diverse opportunities for learning how to program while creating culturally relevant artifacts. However, there is a gap in our understanding of the design processes of learners in these constructionist environments, including how the computational artifacts and their components impact the learning processes and the ways they build meaning and agency with computing. We advocate for research to attend more closely to the materiality of the computational materials to understand how they impact the social and cultural dimensions in which students are learning. In this paper, we present an analysis of 6 high school learners’ experiences within a co-designed arts and computing curriculum. Our analyses highlight how the materiality of the components impacted the ways in which learners developed personal and epistemological connections to computing based on how it enabled them to connect to their interests, represent their ideas, engage with their community, and overcome or navigate around challenges to get to their final designs. We demonstrate how centralizing the materiality in the design of computational construction kits can inform how we support agency and engagement with computing. 

This work contributes to just and pro-social treatment of digital pieceworkers ("crowd collaborators") by reforming the handling of crowd-sourced labor in academic venues. With the rise in automation, crowd collaborators' treatment requires special consideration, as the system often dehumanizes crowd collaborators as components of the “crowd” [41]. Building off efforts to (proxy-)unionize crowd workers and facilitate employment protections on digital piecework platforms, we focus on employers: academic requesters sourcing machine learning (ML) training data. We propose a cover sheet to accompany submission of work that engages crowd collaborators for sourcing (or labeling) ML training data. The guidelines are based on existing calls from worker organizations (e.g., Dynamo [28]); professional data workers in an alternative digital piecework organization; and lived experience as requesters and workers on digital piecework platforms. We seek feedback on the cover sheet from the ACM community 

In this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we ofer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory. 

Interaction design is playing an increasingly prominent role in computing research, while professional user experience roles expand. These forces drive the demand for more de- sign instruction in HCI classrooms. In this paper, we distill the popular approaches to teaching design to undergraduate and graduate students of HCI. Through a review of existing research on design pedagogy, an international survey of 61 HCI educators, and an analysis of popular textbooks, we explore the prominent disciplinary perspectives that shape design education in the HCI classroom. We draw on our analyses to discuss the differences we see in forms of design taught, approaches to adapting design instruction in computing-based courses, and the tensions faced by instructors of these classes. We conclude by arguing for the importance of pedagogical research on design instruction as a vital and foundational area of inquiry in Interaction Design and HCI.