skip to main content
10.1145/2047196.2047199acmconferencesArticle/Chapter ViewAbstractPublication PagesuistConference Proceedingsconference-collections
research-article

Instrumenting the crowd: using implicit behavioral measures to predict task performance

Published: 16 October 2011 Publication History

Abstract

Detecting and correcting low quality submissions in crowdsourcing tasks is an important challenge. Prior work has primarily focused on worker outcomes or reputation, using approaches such as agreement across workers or with a gold standard to evaluate quality. We propose an alternative and complementary technique that focuses on the way workers work rather than the products they produce. Our technique captures behavioral traces from online crowd workers and uses them to predict outcome measures such quality, errors, and the likelihood of cheating. We evaluate the effectiveness of the approach across three contexts including classification, generation, and comprehension tasks. The results indicate that we can build predictive models of task performance based on behavioral traces alone, and that these models generalize to related tasks. Finally, we discuss limitations and extensions of the approach.

References

[1]
Bernstein, M.S., Little, G., Miller, R.C., et al. Soylent: a word processor with a crowd inside. Proceedings of the 23nd annual ACM symposium on User interface soft-ware and technology, ACM (2010), 313--322.
[2]
Callison-Burch, C. Fast, cheap, and creative: Evaluating translation quality using Amazon's Mechanical Turk. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, Association for Computational Linguistics (2009), 286--295.
[3]
Chi, E.I., Pirolli, P., and Pitkow, J. The Scent of a Site : A System for Analyzing and Predicting Information Scent, Usage, and Usability of a Web Site. 2, 1 (2000).
[4]
Dekel, O. and Shamir, O. Vox populi: Collecting high-quality labels from a crowd. COLT 2009: Proceedings of the 22nd Annual Conference on Learning Theory, Citeseer (2009).
[5]
Downs, J.S., Holbrook, M.B., Sheng, S., and Cranor, L.F. Are your participants gaming the system?: screening mechanical turk workers. Proceedings of the 28th international conference on Human factors in computing systems, ACM (2010), 2399--2402.
[6]
Fern, X., Komireddy, C., Grigoreanu, V., and Burnett, M. Mining problem-solving strategies from HCI data. ACM Transactions on Computer-Human Interaction 17, 1 (2010), 1--22.
[7]
Ghazarian, A. and Noorhosseini, S.M. Automatic detection of users' skill levels using high-frequency user interface events. User Modeling and User-Adapted Inter-action 20, 2 (2010), 109--146.
[8]
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I.H. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1 (2009), 10--18.
[9]
Hilbert, D. and Redmiles, D. Extracting usability information from user interface events. ACM Computing Surveys (CSUR) 32, 4 (2000), 384--421.
[10]
Huang, E., Zhang, H., Parkes, D.C., Gajos, K.Z., and Chen, Y. Toward Automatic Task Design : A Progress Report. Proceedings of the ACM SIGKDD workshop on human computation, ACM (2010), 77--85.
[11]
Hurst, A., Hudson, S., Mankoff, J., and Trewin, S. Automatically detecting pointing performance. Proceedings of the 13th, (2008), 11.
[12]
Ipeirotis, P.G., Provost, F., and Wang, J. Quality management on amazon mechanical turk. Proceedings of the ACM SIGKDD workshop on human computation, ACM (2010), 64--67.
[13]
Ivory, M. and Hearst, M.A. The state of the art in automating usability evaluation of user interfaces. ACM Computing Surveys (CSUR) 33, 4 (2001), 470--516.
[14]
Kim, J., Gunn, D., Schuh, E., and Phillips, B. Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems. Proceedings of the twenty-sixth annual SIGCHI conference on Human Factors in Computing Systems, (2008), 443--451.
[15]
Kittur, A., Chi, E., and Suh, B. Crowdsourcing user studies with mechanical Turk. Proceedings of the twenty-sixth annual SIGCHI conference on Human Factors in Computing Systems, (2008), 1509--1512.
[16]
Mason, W., Street, W., and Watts, D.J. Financial Incentives and the " Performance of Crowds ."SIGKDD Explorations 11, 2 (2009), 100--108.
[17]
Rauterberg, M. and Aeppli, R. Learning in Man-Machine Systems : the Measurement of Behavioural and Cognitive Complexity. Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on, IEEE (1995), 4685--4690.
[18]
Rogstadius, J., Kostakos, V., Kittur, A., Smus, B., Laredo, J., and Vukovic, M. An Assessment of Intrinsic and Extrinsic Motivation on Task Performance in Crowdsourcing Markets. (2011).
[19]
Shahaf, D. and Horvitz, E. Generalized task markets for human and machine computation. Proc. 24th AAAI Conference on Artificial Intelligence, (2010).
[20]
Snow, R., O'Connor, B., Jurafsky, D., and Ng, A.Y. Cheap and fast - but is it good?: evaluating non-expert annotations for natural language tasks. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (2008), 254--263.
[21]
2Stieger, S. and Reips, U.-D. What are participants doing while filling in an online questionnaire: A paradata collection tool and an empirical study. Computers in Human Behavior 26, 6 (2010), 1488--1495.
[22]
2Vanderaalst, W., Vandongen, B., Herbst, J., Maruster, L., Schimm, G., and Weijters, a. Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering 47, 2 (2003), 237--267.
[23]
Von Ahn, L. and Dabbish, L. Labeling images with a computer game. Proceedings of the SIGCHI conference on Human factors in computing systems, ACM (2004), 319--326.

Cited By

View all
  • (2024)Snapper: Accelerating Bounding Box Annotation in Object Detection Tasks with Find-and-Snap ToolingProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645162(471-488)Online publication date: 18-Mar-2024
  • (2024)“I Prefer Regular Visitors to Answer My Questions”: Users’ Desired Experiential Background of Contributors for Location-based Crowdsourcing PlatformProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642520(1-18)Online publication date: 11-May-2024
  • (2024)LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing SystemsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642089(1-21)Online publication date: 11-May-2024
  • Show More Cited By

Index Terms

  1. Instrumenting the crowd: using implicit behavioral measures to predict task performance

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UIST '11: Proceedings of the 24th annual ACM symposium on User interface software and technology
    October 2011
    654 pages
    ISBN:9781450307161
    DOI:10.1145/2047196
    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 ACM 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: 16 October 2011

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. crowdsourcing
    2. event logging
    3. mechanical turk
    4. performance
    5. user behavior
    6. user logging

    Qualifiers

    • Research-article

    Conference

    UIST '11

    Acceptance Rates

    UIST '11 Paper Acceptance Rate 67 of 262 submissions, 26%;
    Overall Acceptance Rate 561 of 2,567 submissions, 22%

    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)47
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Snapper: Accelerating Bounding Box Annotation in Object Detection Tasks with Find-and-Snap ToolingProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645162(471-488)Online publication date: 18-Mar-2024
    • (2024)“I Prefer Regular Visitors to Answer My Questions”: Users’ Desired Experiential Background of Contributors for Location-based Crowdsourcing PlatformProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642520(1-18)Online publication date: 11-May-2024
    • (2024)LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing SystemsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642089(1-21)Online publication date: 11-May-2024
    • (2024)Probing into the Usage of Task Fingerprinting in Web Games to Enhance Cognitive Personalization: A Pilot Gamified Experience with Neurodivergent Participants2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH)10.1109/SeGAH61285.2024.10639597(1-8)Online publication date: 7-Aug-2024
    • (2023)A Model for Cognitive Personalization of Microtask DesignSensors10.3390/s2307357123:7(3571)Online publication date: 29-Mar-2023
    • (2023)Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine InteractionApplied Sciences10.3390/app1304219813:4(2198)Online publication date: 8-Feb-2023
    • (2023)CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical ContextProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580847(1-15)Online publication date: 19-Apr-2023
    • (2023)Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01848(20143-20155)Online publication date: 1-Oct-2023
    • (2023)Remote Work, Work Measurement and the State of Work Research in Human-Centred ComputingInteracting with Computers10.1093/iwc/iwad01435:5(725-734)Online publication date: 27-Feb-2023
    • (2022)HumanALProceedings of the Workshop on Human-In-the-Loop Data Analytics10.1145/3546930.3547496(1-8)Online publication date: 12-Jun-2022
    • Show More Cited By

    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