<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>A Window into DataWorks: Developing an Integrated Work-Training Curriculum for Novice Adults</dc:title><dc:creator>Karki, Lara (ORCID:0000000252512425); Priest, Dana (ORCID:0009000086718993); Dubose, James G (ORCID:0000000204884734); Godfrey, Zajerria (ORCID:0009000308176393); Rothschild, Annabel (ORCID:0000000258821608); Shapiro, Ben Rydal (ORCID:0000000180700952); DiSalvo, Betsy (ORCID:0000000318047387)</dc:creator><dc:corporate_author/><dc:editor/><dc:description>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.</dc:description><dc:publisher>ACM</dc:publisher><dc:date>2025-02-12</dc:date><dc:nsf_par_id>10662771</dc:nsf_par_id><dc:journal_name/><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>568 to 574</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1145/3641554.3701826</dc:doi><dcq:identifierAwardId>1951818</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>