Scaling up Data Science Course Projects: A Case Study

Bhavya Bhavya, Jinfeng Xiao, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Large-scale, online Data Science (DS) courses and degree programs are becoming increasingly common due to the global rise in popularity and demand for data scientists. Although project-based learning is integral to gaining hands-on experience in DS education, providing fair, timely, and high-quality feedback on varied projects for a large number of diverse students is challenging. To address those challenges in scaling up the assessment of DS group projects, we integrated multiple techniques, such as rapid feedback, peer grading, graders as meta-reviewers, etc. We present a case study of deploying those strategies for group projects in a large online DS course titled Text Information Systems offered in Fall, 2020. We synthesize our findings from analyzing student and grader survey responses, and share useful lessons and future work.

Original languageEnglish (US)
Title of host publicationL@S 2021 - Proceedings of the 8th ACM Conference on Learning @ Scale
PublisherAssociation for Computing Machinery
Pages311-314
Number of pages4
ISBN (Electronic)9781450382151
DOIs
StatePublished - Jun 8 2021
Event8th Annual ACM Conference on Learning at Scale, L@S 2021 - Virtual, Online, Germany
Duration: Jun 22 2021Jun 25 2021

Publication series

NameL@S 2021 - Proceedings of the 8th ACM Conference on Learning @ Scale

Conference

Conference8th Annual ACM Conference on Learning at Scale, L@S 2021
Country/TerritoryGermany
CityVirtual, Online
Period6/22/216/25/21

Keywords

  • course projects
  • data science education
  • scalable assessment

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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