CS vs non-CS: Analyzing Online Social Behaviors of Data Science Students with Diverse Academic Backgrounds
Author: 
Wensheng WuAuthors Info & Claims
Wensheng WuAuthors Info & ClaimsPage 643
Abstract
Understanding the characteristics of diverse students in a data science program is a key to the success of the program. Towards this goal, we have conducted a study to understand social behaviors of data science students on online platforms, in particular, Piazza and Zoom. Our key findings are: (1) Math/statistics students were least active on Zoom chats, but frequent questioners on Piazza, which suggests that we need to engage these students more during Zoom meetings. (2) Most of endorsed answers on Piazza were contributed by CS students, which suggests a collaborative culture formed between CS and non-CS students. (3) The number of Zoom chats and activities on Piazza correlated positively with student performance, but much more strongly for non-CS students than CS-students.
References
[1]
Niki Gitinabard, Yiqiao Xu, Sarah Heckman, Tiffany Barnes, and Collin F Lynch. 2019. How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses. IEEE Transactions on Learning Technologies, Vol. 12, 2 (2019).
[2]
Wensheng Wu. 2021 a. One Size Doesn't Fit All: Diversifying Data Science Course Projects by Student Background and Interests. In ITiCSE.
[3]
Wensheng Wu. 2021 b. SQL2X: Learning SQL, NoSQL, and MapReduce via Translation. In SIGCSE.
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- CS vs non-CS: Analyzing Online Social Behaviors of Data Science Students with Diverse Academic Backgrounds
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Information & Contributors
Information
Published In
June 2021
109 pages
ISBN:9781450383974
DOI:10.1145/3456565
- General Chairs:
- Carsten Schulte,
- Brett A. Becker,
- Program Chairs:
- Monica Divitini,
- Erik Barendsen
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Association for Computing Machinery
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Published: 26 June 2021
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ITiCSE 2021
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ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education
June 26 - July 1, 2021
Virtual Event, Germany
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Overall Acceptance Rate 552 of 1,613 submissions, 34%
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