@inproceedings{919,
  author = {Lara Schenck and Betsy DiSalvo},
  title = {From Data Work to Data Science: Getting Past the Gatekeepers},
  abstract = {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].},
  year = {2023},
  chapter = {1},
  pages = {2},
  month = {08},
  publisher = {ACM},
  isbn = {9781450399753},
  url = {https://par.nsf.gov/biblio/10469964},
  doi = {10.1145/3568812.3603468},
}