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Scaling Up Data Science Course Projects: A Case Study

Authors:

Bhavya Bhavya,

Jinfeng Xiao,

Chengxiang ZhaiAuthors Info & Claims

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

Pages 311 - 314

https://doi.org/10.1145/3430895.3460168

Published: 08 June 2021 Publication History

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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.

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References

[1]

Gabriel Badea and Elvira Popescu. 2019. Using LearnEval Peer Assessment Platform in Project-Based Learning Settings: A First Experience Report. In 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, 1--5.

Google Scholar

[2]

Bhavya, Assma Boughoula, Aaron Green, and ChengXiang Zhai. 2020. Collective development of large scale data science products via modularized assignments: An experience report. In Proceedings of the 51st ACM SIGCSE. 1200--1206.

Google Scholar

[3]

Julia Cambre, Scott Klemmer, and Chinmay Kulkarni. 2018. Juxtapeer: Comparative peer review yields higher quality feedback and promotes deeper reflection. In Proceedings of the 2018 CHI. 1--13.

Digital Library

Google Scholar

[4]

Chase Geigle, Ismini Lourentzou, Hari Sundaram, and ChengXiang Zhai. 2018. CLaDS: a cloud-based virtual lab for the delivery of scalable hands-on assignments for practical data science education. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. 176--181.

Digital Library

Google Scholar

[5]

David A Joyner, Wade Ashby, Liam Irish, Yeeling Lam, Jacob Langston, Isabel Lupiani, Mike Lustig, Paige Pettoruto, Dana Sheahen, Angela Smiley, and others. 2016. Graders as meta-reviewers: Simultaneously scaling and improving expert evaluation for large online classrooms. In Proceedings of the Third (2016) ACM L@S. 399--408.

Digital Library

Google Scholar

[6]

Chinmay Kulkarni, Koh Pang Wei, Huy Le, Daniel Chia, Kathryn Papadopoulos, Justin Cheng, Daphne Koller, and Scott R Klemmer. 2013. Peer and self assessment in massive online classes. ACM TOCHI 20, 6 (2013), 1--31.

Digital Library

Google Scholar

[7]

Chinmay E Kulkarni, Michael S Bernstein, and Scott R Klemmer. 2015. PeerStudio: rapid peer feedback emphasizes revision and improves performance. In Proceedings of the second (2015) ACM L@S. 75--84.

Digital Library

Google Scholar

[8]

Henry B Mann and Donald R Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics (1947), 50--60.

Google Scholar

[9]

Bina Ramamurthy. 2016. A practical and sustainable model for learning and teaching data science. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education. 169--174.

Digital Library

Google Scholar

[10]

Manikandan Ravikiran. 2020. Systematic Review of Approaches to Improve Peer Assessment at Scale. arXiv preprint arXiv:2001.10617 (2020).

Google Scholar

[11]

Alfred P Rovai and Mervyn J Wighting. 2005. Feelings of alienation and community among higher education students in a virtual classroom. The Internet and higher education 8, 2 (2005), 97--110.

Google Scholar

[12]

Maya Usher and Miri Barak. 2018. Peer assessment in a project-based engineering course: comparing between on-campus and online learning environments. Assessment & Evaluation in Higher Education 43, 5 (2018), 745--759.

Crossref

Google Scholar

Cited By

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Zhang LZong YChen TMa YAn SZhang L(2023)A Peer Review Approach to Grading Projects in Computer Courses2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343176(1-7)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343176
Zhang LChen TZong YZhang L(2023)Research to Practice of Blended Learning in Computer Programming Course2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342940(1-5)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10342940

Index Terms

Scaling Up Data Science Course Projects: A Case Study
1. Social and professional topics
  1. Professional topics
    1. Computing education

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Information & Contributors

Information

Published In

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

June 2021

380 pages

ISBN:9781450382151

DOI:10.1145/3430895

General Chair:
Christoph Meinel
Hasso Plattner Institute for Digital Engineering Potsdam, Germany
,
Program Chairs:
Mar Pérez-Sanagustín
Paul Sabatier University Toulouse III, France
,
Marcus Specht
TU Delft, Netherlands
,
Amy Ogan
Carnegie Mellon University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 June 2021

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L@S '21

L@S '21: Eighth (2021) ACM Conference on Learning @ Scale

June 22 - 25, 2021

Virtual Event, Germany

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Overall Acceptance Rate 117 of 440 submissions, 27%

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Zhang LZong YChen TMa YAn SZhang L(2023)A Peer Review Approach to Grading Projects in Computer Courses2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343176(1-7)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343176
Zhang LChen TZong YZhang L(2023)Research to Practice of Blended Learning in Computer Programming Course2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342940(1-5)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10342940

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Utilization of Rubrics for Self-assessment of Computer Science Students Enrolled in a Research Writing Course

A comparison study of computer-assisted instruction using interactive software versus traditional instruction in a college precalculus course

Assessment-via-Teaching: Exploring an Alternative Assessment Strategy in Undergraduate Introductory Data Science Course