Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2858036.2858221acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

A Live, Multiple-Representation Probabilistic Programming Environment for Novices

Published: 07 May 2016 Publication History

Abstract

We present a live, multiple-representation novice environment for probabilistic programming based on the Infer.NET language. When compared to a text-only editor in a controlled experiment on 16 participants, our system showed a significant reduction in keystrokes during introductory probabilistic programming exercises, and subsequently, a significant improvement in program description and debugging tasks as measured by task time, keystrokes and deletions.

Supplementary Material

ZIP File (pn1017-file4.zip)
pn1017-file4.zip
suppl.mov (pn1017-file3.mp4)
Supplemental video
MP4 File (p2533-gorinova.mp4)

References

[1]
2015. Bayesia. (2015). http://www.bayesia.com/ Accessed: January 8, 2016.
[2]
2015. F# Compiler Services. http: //fsharp.github.io/FSharp.Compiler.Service/. (2015). Accessed: January 8, 2016.
[3]
2015. Light Table. http://lighttable.com/. (2015). Accessed: January 8, 2016.
[4]
2015. Netica. (2015). https://www.norsys.com/netica.html Accessed: January 8, 2016.
[5]
Christopher M Bishop, David Spiegelhalter, and John Winn. 2002. VIBES: A variational inference engine for Bayesian networks. In Advances in neural information processing systems. 777--784.
[6]
Richard Bornat, Saeed Dehnadi, and others. 2008. Mental models, consistency and programming aptitude. In Proceedings of the tenth conference on Australasian computing education-Volume 78. Australian Computer Society, Inc., 53--61.
[7]
Wray L. Buntine. 1994. Operations for learning with graphical models. JAIR 2 (1994), 159--225.
[8]
Sebastian Burckhardt, Manuel Fahndrich, Peli de Halleux, Jun Kato, Sean McDirmid, Michal Moskal, and Nikolai Tillmann. 2013. It's Alive! Continuous Feedback in UI Programming. In PLDI. ACM SIGPLAN. http://research.microsoft.com/apps/ pubs/default.aspx?id=189242
[9]
Andrew D Gordon, Thore Graepel, Nicolas Rolland, Claudio Russo, Johannes Borgstrom, and John Guiver. 2013. Tabular: A Schema-Driven Probabilistic Programming Language. Technical Report MSR-TR-2013--118. http://research.microsoft. com/apps/pubs/default.aspx?id=204661
[10]
F.R. Kschischang, B.J. Frey, and H.-A. Loeliger. 2001. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47, 2 (2001), 498--519.
[11]
David J Lunn, Andrew Thomas, Nicky Best, and David Spiegelhalter. 2000. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and computing 10, 4 (2000), 325--337.
[12]
T Minka, J M Winn, J P Guiver, S Webster, Y Zaykov, B Yangel, A Spengler, and J Bronskill. 2014. Infer.NET 2.6. (2014).
[13]
Kevin Murphy. 1998. A brief introduction to graphical models and Bayesian networks. (1998).
[14]
P Sedlmeier and G Gigerenzer. 2001. Teaching Bayesian reasoning in less than two hours. Journal of experimental psychology. General 130, 3 (Sept. 2001), 380--400. http://www.ncbi.nlm.nih.gov/ /11561916
[15]
Alistair Stead and Alan F. Blackwell. Learning Syntax as Notational Expertise when using DrawBridge. In Psychology of Programming Interest Group Annual Conference 2014. 41.
[16]
Kozo Sugiyama, Shojiro Tagawa, and Mitsuhiko Toda. 1981. Methods for visual understanding of hierarchical system structures. Systems, Man and Cybernetics, IEEE Transactions on 11, 2 (1981), 109--125.
[17]
Steven L Tanimoto. 1990. VIVA: A visual language for image processing. Journal of Visual Languages & Computing 1, 2 (1990), 127--139.
[18]
Uan-Diego Zapata-Rivera and Jim E. Greer. 2004. Interacting with Inspectable Bayesian Student Models. International Journal of Artificial Intelligence in Education Volume 14, Number 2/2004 - IOS Press (2004). http://iospress.metapress.com/content/rj0wwc454vvll8xn/

Cited By

View all
  • (2024)Language-Agnostic Static Analysis of Probabilistic ProgramsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695031(78-90)Online publication date: 27-Oct-2024
  • (2023)Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?Proceedings of the 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3622758.3622882(153-167)Online publication date: 18-Oct-2023
  • (2023)Domain-Specific Probabilistic Programming with Multiverse Explorer2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL-HCC57772.2023.00022(124-132)Online publication date: 3-Oct-2023
  • Show More Cited By

Index Terms

  1. A Live, Multiple-Representation Probabilistic Programming Environment for Novices

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
      May 2016
      6108 pages
      ISBN:9781450333627
      DOI:10.1145/2858036
      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].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 May 2016

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. computational thinking
      2. development environment
      3. multiple representation
      4. probabilistic programming
      5. visual languages

      Qualifiers

      • Research-article

      Conference

      CHI'16
      Sponsor:
      CHI'16: CHI Conference on Human Factors in Computing Systems
      May 7 - 12, 2016
      California, San Jose, USA

      Acceptance Rates

      CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;
      Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

      Upcoming Conference

      CHI 2025
      ACM CHI Conference on Human Factors in Computing Systems
      April 26 - May 1, 2025
      Yokohama , Japan

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)30
      • Downloads (Last 6 weeks)2
      Reflects downloads up to 25 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Language-Agnostic Static Analysis of Probabilistic ProgramsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695031(78-90)Online publication date: 27-Oct-2024
      • (2023)Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?Proceedings of the 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3622758.3622882(153-167)Online publication date: 18-Oct-2023
      • (2023)Domain-Specific Probabilistic Programming with Multiverse Explorer2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL-HCC57772.2023.00022(124-132)Online publication date: 3-Oct-2023
      • (2023)Octave: An End-User Programming Environment for Analysis of Spatiotemporal Data for Construction Students2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL-HCC57772.2023.00014(51-59)Online publication date: 3-Oct-2023
      • (2023)A visual analytics workflow for probabilistic modelingVisual Informatics10.1016/j.visinf.2023.05.0017:2(72-84)Online publication date: Jun-2023
      • (2022)Student Misconceptions about Loops in Introductory Programming Courses and the Influence of Representations2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962545(1-5)Online publication date: 8-Oct-2022
      • (2019)Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministicProceedings of the ACM on Programming Languages10.1145/32903483:POPL(1-30)Online publication date: 2-Jan-2019
      • (2019)End-User Probabilistic ProgrammingQuantitative Evaluation of Systems10.1007/978-3-030-30281-8_1(3-24)Online publication date: 4-Sep-2019
      • (2018)Calculation View: multiple-representation editing in spreadsheets2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VLHCC.2018.8506584(85-93)Online publication date: Oct-2018
      • (2018)Interpreting and Navigating Multiple Representations for Computational Thinking in a Robotics Programming EnvironmentJournal for STEM Education Research10.1007/s41979-018-0006-21:1-2(119-147)Online publication date: 6-Nov-2018

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media