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Data-Informed Learning Design in a Computer Science Course

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Learning, Design, and Technology

Abstract

This case study chapter relates the experience of develo**, evaluating, and iterating a media-rich asynchronous online course in light of increasingly sophisticated data-driven measures of course quality. The case will begin by introducing two guiding analytics frameworks: depth of use (DOU) and video analytics housed in the video content management system at the university. DOU is a measurement of the extent to which LMS tools and elements to promote learner engagement are incorporated into a course site, and it has shown a statistically significant positive correlation with several indicators of student engagement, as well as with mean final student grade in a given course (Hassan et al., Depth of use: An empirical framework to help faculty gauge the relative impact of learning management systems tools. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/2543882.2543885, 2020). Video analytics are used to determine student viewing patterns and glean insights into how the instructional videos could be improved. Additionally, the case will explore the successes and challenges of designing an undergraduate computer science course to meet university priorities. This case will have implications for a wide variety of readers, including but not limited to administrators, professors, and instructional designers.

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Author information

Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, USA

    Daron Williams, Larry Cox II, Margaret Ellis, Taha Hassan, M. Aaron Bond, Quinn Warnick, Virginia Clark, Daniel Yaffe, Molly Domino & Derek Haqq

  2. Department of Computer Science, Virginia Tech, Blacksburg, VA, USA

    Bob Edmison

Authors
  1. Daron Williams
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  2. Larry Cox II
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  3. Margaret Ellis
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  4. Bob Edmison
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  5. Taha Hassan
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  6. M. Aaron Bond
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  7. Quinn Warnick
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  8. Virginia Clark
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  9. Daniel Yaffe
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  10. Molly Domino
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  11. Derek Haqq
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Corresponding authors

Correspondence to Daron Williams or M. Aaron Bond .

Editor information

Editors and Affiliations

  1. College of Info, Ste G150, Univ of North Texas, Denton, TX, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, VA, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, KS, USA

    Marcus D. Childress

Section Editor information

  1. Organization, Information, and Learning Sciences, University of New Mexico, Albuquerque, NM, USA

    Stephanie L. Moore Ph.D.

  2. Instructional Psychology and Technology, Brigham Young University, Provo, UT, USA

    Heather Leary

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Williams, D. et al. (2022). Data-Informed Learning Design in a Computer Science Course. In: Spector, M.J., Lockee, B.B., Childress, M.D. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_176-1

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  • DOI: https://doi.org/10.1007/978-3-319-17727-4_176-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17727-4

  • Online ISBN: 978-3-319-17727-4

  • eBook Packages: Springer Reference EducationReference Module Humanities and Social SciencesReference Module Education

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