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Diversity-focused Online Professional Development for Community College Computing Faculty: Participant Motivations and Perceptions

Published:21 February 2018Publication History

ABSTRACT

Although computing occupations increasingly dominate the workforce, the computing discipline does not attract sufficient and diverse students to meet societal workforce needs. LIGHTHOUSE FOR CS is a collection of professional development efforts seeking to increase computing diversity through faculty education. LIGHTHOUSE CC provides an open online course extending a decade of successful face-to-face TAPESTRY workshops. LIGHTHOUSE CC targets community college computing faculty, building off of research suggesting that online professional development can be as effective as in-person professional development. We believe it is one of the first attempts to validate that finding for community college computing faculty. The paper examines participant motivations and perceptions of online and face-to-face diversity-focused professional development for community college computing educators. To meet the needs of the community college computing faculty, we began with a needs assessment survey. Its feedback and prior face-to-face workshop experiences were integrated with several instructional design principles and theories to engage community college faculty, notably the ARCS motivational design model (Keller, 1987) and the Community of Practice model (Wenger, 1998). Project analyses involved both surveys and focus group interviews. The paper also provides directions for future improvements and suggestions for others who are considering online or diversity-focused professional development.

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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

        Copyright © 2018 ACM

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        Publication History

        • Published: 21 February 2018

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        SIGCSE '18 Paper Acceptance Rate161of459submissions,35%Overall Acceptance Rate1,595of4,542submissions,35%

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