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Challenges and Approaches for Data Collection to Understand Student Retention: (Abstract Only)

Published: 21 February 2018 Publication History

Abstract

For many years, computing faculty have devoted substantial time and energy to the retention of diverse populations. But how are we doing really? The ACM Retention Committee has identified at least 5 populations of interest in tracking student retention: * Students who start college expecting to major in computing. * Students who enter college with some interest in computing, but also with other interests. * Students who enter college with interests outside computing, but who take computing early as part of a broad education. * Students who enter college with little or no interest in computing, but need a computing course to satisfy a general education requirement or a prerequisite in another discipline. * Students who transfer into a four-year university from a two-year college, partway into a computer science program. In practice, each group has different characteristics, and retention rates may vary dramatically. On some campuses, gathering data for the first group may be manageable--particularly if students declare majors as they enter college. Data collection and tracking for others is difficult, since these populations may not be known in early years. This BoF will identify approaches for tracking students and for exploring retention rates. Further, this BoF will encourage sharing and brainstorming for further mechanisms to help data collection. As we better identify retention rates among various populations, the ACM Retention Committee hopes we can better understand obstacles and opportunities related to retention. Session Agenda: Context/Introduction, Data most relevant locally, What data are currently tracked, Thoughts about a common data gathering instrument

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  1. Challenges and Approaches for Data Collection to Understand Student Retention: (Abstract Only)

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    cover image ACM Conferences
    SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education
    February 2018
    1174 pages
    ISBN:9781450351034
    DOI:10.1145/3159450
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 February 2018

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    1. data collection
    2. student retention

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    SIGCSE '18
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    Acceptance Rates

    SIGCSE '18 Paper Acceptance Rate 161 of 459 submissions, 35%;
    Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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    SIGCSE Virtual 2024
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    December 5 - 8, 2024
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