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Collecting, Analyzing, and Acting on Intersectional, Longitudinal Data and Pass/Fail/Withdraw Rates in Computing Courses

Published: 07 March 2024 Publication History

Abstract

We present the Center for Inclusive Computing's data collection and visualization system, which enables computing departments to track and visualize their enrollment and course outcome data intersectionally and longitudinally. The system tracks the impact of institutional changes in how computing (particularly the introductory sequence) is discovered and experienced by undergraduates as measured by course outcome and persistence data. To date we have worked with and collected data from 52 U.S. computing departments. Collected data spans 2018-present and contains term-by- term, intersectional course enrollment and outcome data for CS 1-3, while also tracking declared majors and persistence to graduation. Drawing on our experience working with these universities we present guidelines for the analysis of intersectional, longitudinal data alongside our recommendations for actionable next steps. We present three case studies grounded in an analysis of CS1, demon- strating how an institution can understand their own computing program and develop interventions-specifically with an eye toward broadening participation in computing.

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    cover image ACM Conferences
    SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
    March 2024
    1583 pages
    ISBN:9798400704239
    DOI:10.1145/3626252
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    Published: 07 March 2024

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    1. broadening participation in computing
    2. case studies
    3. cs 1
    4. demographic data
    5. intersectionality

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