skip to main content
10.1145/2658779.2661167acmconferencesArticle/Chapter ViewAbstractPublication PagesuistConference Proceedingsconference-collections
extended-abstract

Interacting with massive numbers of student solutions

Published: 05 October 2014 Publication History

Abstract

When teaching programming or hardware design, it is pedagogically valuable for students to generate examples of functions, circuits, or system designs. Teachers can be overwhelmed by these types of student submissions when running large residential or recently released massive online courses. The underlying distribution of student solutions submitted in response to a particular assignment may be complex, but the newly available volume of student solutions represents a denser sampling of that distribution. Working with large datasets of students' solutions, I am building systems with user interfaces that allow teachers to explore the variety of their students' correct and incorrect solutions. Forum posts, grading rubrics, and automatic graders can be based on student solution data, and turn massive engineering and computer science classrooms into useful insight and feedback for teachers. In the development process, I hope to describe essential design principles for such systems.

Supplementary Material

suppl.mov (uistdc0112-file3.mp4)
Supplemental video

References

[1]
Basu, S., Jacobs, C., and Vanderwende, L. Powergrading: a clustering approach to amplify human effort for short answer grading. TACL 1 (2013), 391--402.
[2]
Boud, D., Cohen, R., and Sampson, J. Peer learning in higher education: Learning from and with each other. Routledge, 2014.
[3]
Brooks, M., Basu, S., Jacobs, C., and Vanderwende, L. Divide and correct: using clusters to grade short answers at scale. In Learning at Scale (2014), 89--98.
[4]
Glassman, E. L., Gulley, N., and Miller, R. C. Toward facilitating assistance to students attempting engineering design problems. In Proceedings of the Tenth Annual International Conference on International Computing Education Research, ICER '13, ACM (New York, NY, USA, 2013).
[5]
Glassman, E. L., Scott, J., Singh, R., Guo, P. J., and Miller, R. C. Overcode: Visualizing variation in student solutions to programming problems at scale (in submission). ACM Trans. Comput.-Hum. Interact. (2014).
[6]
Glassman, E. L., Singh, R., Gulley, N., and Miller, R. C. Feature engineering for clustering student solutions. CHI 2014 Learning Innovations at Scale Workshop, 2014.
[7]
Huang, J., Piech, C., Nguyen, A., and Guibas, L. J. Syntactic and functional variability of a million code submissions in a machine learning mooc. In AIED Workshops (2013).
[8]
Muralidharan, A., and Hearst, M. Wordseer: Exploring language use in literary text. Fifth Workshop on Human-Computer Interaction and Information Retrieval (2011).
[9]
Muralidharan, A., and Hearst, M. A. Supporting exploratory text analysis in literature study. Literary and linguistic computing 28, 2 (2013), 283--295.
[10]
Muralidharan, A. S., Hearst, M. A., and Fan, C. Wordseer: a knowledge synthesis environment for textual data. In CIKM (2013), 2533--2536.
[11]
Nguyen, A., Piech, C., Huang, J., and Guibas, L. J. Codewebs: scalable homework search for massive open online programming courses. In WWW (2014), 491--502.
[12]
Rzeszotarski, J. M., and Kittur, A. Crowdscape: interactively visualizing user behavior and output. In UIST (2012), 55--62.
[13]
Shasha, D., Wang, J.-L., Zhang, K., and Shih, F. Y. Exact and approximate algorithms for unordered tree matching. IEEE Transactions on Systems, Man and Cybernetics 24, 4 (1994), 668--678.

Cited By

View all

Index Terms

  1. Interacting with massive numbers of student solutions

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UIST '14 Adjunct: Adjunct Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology
    October 2014
    150 pages
    ISBN:9781450330688
    DOI:10.1145/2658779
    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.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 October 2014

    Check for updates

    Author Tags

    1. MOOCs
    2. data mining
    3. programming exercises

    Qualifiers

    • Extended-abstract

    Funding Sources

    Conference

    UIST '14

    Acceptance Rates

    UIST '14 Adjunct Paper Acceptance Rate 74 of 333 submissions, 22%;
    Overall Acceptance Rate 355 of 1,733 submissions, 20%

    Upcoming Conference

    UIST '25
    The 38th Annual ACM Symposium on User Interface Software and Technology
    September 28 - October 1, 2025
    Busan , Republic of Korea

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 23 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all

    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