Computing education is often confined to the context of formal education or after-school programs; however, there is a growing industry built around adult education, including workshops, coding intensives, online learning, and apprenticeship programs. Amidst these efforts, little research has explored the workplace as a site for novice adult learners to develop computing skills. In this experience report, we present an integrated training curriculum for adults at DataWorks, an organization that trains and employs novice adults from groups historically underrepresented in computing who seek to advance their career through on-the-job learning. ''Data Fellows'' are hired to complete client projects by providing data services for local organizations, nonprofits, and businesses. Training is integrated into employees' weekly responsibilities at DataWorks, and the curriculum consists of four modules: Microsoft Excel, Critical Data Literacy, Python Fundamentals, and Career Development. In this report, we reflect holistically on the evolution of the curriculum over three years. We distill our reflection into insights to inform other integrated training programs that aim to equip novice adults with computing skills in the workplace.

LinkedIn is central to salaried job search and professional networking. In a career development program for adults seeking upward socioeconomic mobility through middle-wage computing work, we aimed to use LinkedIn to find and develop new social ties. However, we could not use the platform for this purpose. Through a participatory research approach, we formed a research team with diverse positionalities to understand why LinkedIn was difficult to use and how it could be better for our program. We analyzed recorded walk-throughs and confirmed our findings with two years of ethnographic field notes and written reflections. Our findings demonstrate that LinkedIn's embedded algorithms and interface design prioritize users with large networks who can afford a LinkedIn Premium subscription. We argue that such platform-embedded power differentials lead to platform-delivered microaggressions. Non-Premium users and users with small networks must endure microaggressions to participate in the salaried labor market. We argue the politics of LinkedIn as a platform are such that its embedded power differentials are beyond our control and unlikely to change. Therefore, we recommend sociotechnical coping and mitigation strategies for career development programs in lieu of design implications for LinkedIn or similar platforms. We contribute a detailed example of how a technology reinforces pre-existing privilege without users' knowledge.

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].