Improving Current and Future Offerings of a Data Science Course through Large-Scale Observation of Students

Tabitha Belshee, Adam Chang, Nebil Ibrahim, Mikako Inaba, Nikoo Karbassi, Angelo Kayser-Browne, Hye Jee Kim, Rachel Kim, Seungjae Ryan Lee, Natalia Orlovsky, Michael Guerzhoy

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

Abstract

We delivered a large Introduction to Data Science course with a team of undergraduate Teaching Assistant-Researchers (TARs) who both helped students in the lab and collected qualitative observations about student learning. The TARs were concurrently participating in a senior-level Pedagogy of Data Science seminar. We present a strategy for collecting and systematizing our observations, and present actionable conclusions that can be used to improve future offerings of the course. We present evidence that suggests that participating in the study raised student performance on an end-of-semester test by 0.4σ (CI: [0.1σ, 1.8σ], p = 0.02), where σ is the class standard deviation.

Original languageEnglish (US)
Title of host publicationSIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery, Inc
Pages1280
Number of pages1
ISBN (Electronic)9781450380621
DOIs
StatePublished - Mar 3 2021
Event52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021 - Virtual, Online, United States
Duration: Mar 13 2021Mar 20 2021

Publication series

NameSIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

Conference

Conference52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021
CountryUnited States
CityVirtual, Online
Period3/13/213/20/21

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Education

Keywords

  • cs1
  • data science
  • pedagogical content knowledge
  • pedagogy
  • qualitative methods

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