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A Generic Framework for Engaging Online Data Sources in Introductory Programming Courses

Published: 11 July 2016 Publication History

Abstract

This paper presents work on a code framework and methodology to facilitate the introduction of large, real-time, online data sources into introductory (or advanced) Computer Science courses. The framework is generic in the sense that no prior scaffolding or template specification is needed to make the data accessible, as long as the source uses a standard format such as XML, CSV, or JSON. The implementation described here maintains minimal syntactic overhead while relieving novice programmers from low-level issues of parsing raw data from a web-based data source. It interfaces directly with data structures and representations defined by the students themselves, rather than predefined and supplied by the library. Together, these features allow students and instructors to focus on algorithmic aspects of processing a wide variety of live and large data sources, without having to deal with low-level connection, parsing, extraction, and data binding. The library, available at http://cs.berry.edu/big-data, has been used in an introductory programming course based on Processing.

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R. E. Anderson, M. D. Ernst, R. Ordóñez, P. Pham, and B. Tribelhorn. A data programming CS1 course. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE '15, pages 150--155, New York, NY, USA, 2015. ACM.
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A. C. Bart. Situating computational thinking with big data: Pedagogy and technology (abstract only). In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE '15, pages 719--719, New York, NY, USA, 2015. ACM.
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A. C. Bart, E. Tilevich, S. Hall, T. Allevato, and C. A. Shaffer. Transforming introductory computer science projects via real-time web data. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE '14, pages 289--294, New York, NY, USA, 2014. ACM.
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  • (2023)AI and parallelism in CS1Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i13.26876(15798-15806)Online publication date: 7-Feb-2023
  • (2020)Lightweight Automated Structure Inference and Binding of Data Sources to Predefined Data TypesProceedings of the 2020 ACM Southeast Conference10.1145/3374135.3385284(71-78)Online publication date: 2-Apr-2020
  • (2018)Automated inference of fixed-width data formatsJournal of Computing Sciences in Colleges10.5555/3282588.328261634:2(199-207)Online publication date: 1-Dec-2018
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cover image ACM Conferences
ITiCSE '16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education
July 2016
394 pages
ISBN:9781450342315
DOI:10.1145/2899415
Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 11 July 2016

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Author Tags

  1. introductory cs
  2. library
  3. open data
  4. web services
  5. xml

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  • Research-article

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ITiCSE '16 Paper Acceptance Rate 56 of 147 submissions, 38%;
Overall Acceptance Rate 552 of 1,613 submissions, 34%

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View all
  • (2023)AI and parallelism in CS1Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i13.26876(15798-15806)Online publication date: 7-Feb-2023
  • (2020)Lightweight Automated Structure Inference and Binding of Data Sources to Predefined Data TypesProceedings of the 2020 ACM Southeast Conference10.1145/3374135.3385284(71-78)Online publication date: 2-Apr-2020
  • (2018)Automated inference of fixed-width data formatsJournal of Computing Sciences in Colleges10.5555/3282588.328261634:2(199-207)Online publication date: 1-Dec-2018
  • (2018)Real live data for CS coursesJournal of Computing Sciences in Colleges10.5555/3205191.320521633:6(165-167)Online publication date: 1-Jun-2018
  • (2018)Preparing, Visualizing, and Using Real-world Data in Introductory CoursesProceedings of the 49th ACM Technical Symposium on Computer Science Education10.1145/3159450.3159616(676-677)Online publication date: 21-Feb-2018
  • (2017)Teaching CS courses with real live dataJournal of Computing Sciences in Colleges10.5555/3144645.314467733:2(213-214)Online publication date: 1-Dec-2017
  • (2017)Computing with CORGISACM Inroads10.1145/3095781.30177088:2(66-72)Online publication date: 8-Mar-2017
  • (2017)Computing with CORGISProceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education10.1145/3017680.3017708(57-62)Online publication date: 8-Mar-2017

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