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Preparing, Visualizing, and Using Real-world Data in Introductory Courses

Published: 21 February 2018 Publication History

Abstract

Working with real-world data has increasingly become a popular context for introductory computing courses. As a valuable 21st century skill, preparing students to be able to divine meaning from data can be useful to their long-term careers. Because Data Science aligns so closely with computing, many of the topics and problems it affords as a context can support the core learning objectives in introductory computing classes. In many instances, incorporating a real-world dataset to provide concrete context for an activity or assignment can improve student engagement and understanding of the abstract educational content being presented. However, there are many problems inherent to bringing real-world data into introductory courses. How do instructors, with finite amounts of time and energy, find and prepare suitable datasets for their pedagogical needs? Once the datasets are ready, how can students conveniently interact with and draw meaning from the datasets, especially when they are used in complex projects that are typical of later introductory courses? On the other hand, how does an instructor balance the complexities of using real-world datasets in the classroom, making sure that students appreciate the meaningfulness of course activities and their connection to learning objectives? This panel brings together experts with experience in using real-world data in introductory computing courses. Each panelist provides unique perspectives and skills to the problem of preparing, interacting, visualizing, and using pedagogical datasets. This panel should be of particular interest to instructors who are considering integrating current and real-world data into their assignments and projects, and to educational developers who want to create and manage datasets for pedagogical purposes. The panel will follow a conventional format: 5 minutes of introduction, 10 minutes for each panelist to present, and then 30 minutes for audience Q&A.

References

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Ruth E. Anderson, Michael D. Ernst, Robert Ordó nez, Paul Pham, and Ben Tribelhorn. 2015. A Data Programming CS1 Course. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15). ACM, New York, NY, USA, 150--155.
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Austin Cory Bart, Ryan Whitcomb, Dennis Kafura, Clifford A Shaffer, and Eli Tilevich. 2017. Computing with corgis: Diverse, real-world datasets for introductory computing Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. 57--62.
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David Burlinson, Mihai Mehedint, Chris Grafer, Kalpathi Subramanian, Jamie Payton, Paula Goolkasian, Michael Youngblood, and Robert Kosara. 2016. BRIDGES: A System to Enable Creation of Engaging Data Structures Assignments with Real-World Data and Visualizations. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE '16). ACM, New York, NY, USA, 18--23.
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Thomas H Davenport and DJ Patil. 2012. Data Scientist: The Sexiest Job of the 21st Century-A new breed of professional holds the key to capitalizing on big data opportunities. But these specialists aren't easy to find--And the competition for them is fierce. Harvard Business Review (2012), 70.
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Peter DePasquale. {n. d.}. Exploiting On-line Data Sources As the Basis of Programming Projects Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (SIGCSE '06). 283--287.
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Olaf A. Hall-Holt and Kevin R. Sanft. {n. d.}. Statistics-infused Introduction to Computer Science Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15). 138--143.
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Nadeem Abdul Hamid. 2016. A Generic Framework for Engaging Online Data Sources in Introductory Programming Courses Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '16). ACM, New York, NY, USA, 136--141.
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Daniel E. Stevenson and Paul J. Wagner. {n. d.}. Developing Real-world Programming Assignments for CS1 Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITICSE '06). 158--162.
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David G. Sullivan. {n. d.}. A Data-centric Introduction to Computer Science for Non-majors Proceeding of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE '13). 71--76.

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cover image ACM Conferences
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education
February 2018
1174 pages
ISBN:9781450351034
DOI:10.1145/3159450
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

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Publication History

Published: 21 February 2018

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  1. data science
  2. real world data
  3. visualization

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SIGCSE '18 Paper Acceptance Rate 161 of 459 submissions, 35%;
Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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