Discovering Knowledge Sharing Patterns of Blind People Pursuing STEM Disciplines: Data Science and Computational Linguistics on Large-Scale Email Corpora
Tuesday, September 15, 2020 (Central Time) — 11:45AM - 12:30PM
Despite the comprehensive need for STEM (Science, Technology, Engineering, and Mathematics) literacy across formal and informal learning, students with disabilities, who are increasingly participating in regular classrooms, experience significant difficulties in this trend. Blind students, in particular, have become even more disenfranchised with the visually-oriented STEM practices. While several attempts have been made to address STEM accessibility issues for the blind, existing studies have been primarily limited to either usability field test or special curriculum design from a top-down approach taken by researchers. There is little attention devoted to bottom-up research where the lived experiences of blind STEM learners, as central storytellers, are naturally portrayed to yield their own challenges and shared cultures. This study aims to discover collective knowledge-sharing patterns and informal learning cultures of blind individuals pursuing STEM disciplines as captured through computer-mediated mailing listservs. Using the National Federation of the Blind mailing list, one of the world largest online mailing communities for the blind, this research will conduct longitudinal quantitative ethnography for the four STEM-oriented listserv archives in the public domain (i.e., NFB-Science and Engineering; Computer Science; Artists-Making-Art; BlindMath) between December 2009 and December 2019 to develop a comprehensive understanding of learning experiences voiced by blind individuals. The findings of this dissertation should make an important contribution to the field of the Learning Sciences by discussing “How Blind People Learn STEM” and “How a Blind Learning Scientist Researches” through rigorous, reliable, and reproducible methods offered by computer-assisted text analysis coupled with humanistic deep reading of the corpus.