@inproceedings{76bf61ee8aec49efadeb1cd89442352b,
title = "Improving Current and Future Offerings of a Data Science Course through Large-Scale Observation of Students",
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.",
keywords = "cs1, data science, pedagogical content knowledge, pedagogy, qualitative methods",
author = "Tabitha Belshee and Adam Chang and Nebil Ibrahim and Mikako Inaba and Nikoo Karbassi and Angelo Kayser-Browne and Kim, {Hye Jee} and Rachel Kim and Lee, {Seungjae Ryan} and Natalia Orlovsky and Michael Guerzhoy",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021 ; Conference date: 13-03-2021 Through 20-03-2021",
year = "2021",
month = mar,
day = "3",
doi = "10.1145/3408877.3439640",
language = "English (US)",
series = "SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education",
publisher = "Association for Computing Machinery, Inc",
pages = "1280",
booktitle = "SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education",
}