skip to main content
research-article

Better Understanding Procedural Search Tasks: Perceptions, Behaviors, and Challenges

Authors Info & Claims
Published:29 December 2023Publication History
Skip Abstract Section

Abstract

People often search for information to acquire procedural knowledge–“how to” knowledge about step-by-step procedures, methods, algorithms, techniques, heuristics, and skills. A procedural search task might involve implementing a solution to a problem, evaluating different approaches to a problem, and brainstorming on the types of problems that can be solved with a specific resource. We report on a study (N=36) that aimed to better understand how people search for procedural knowledge. Much research has investigated how search task characteristics impact people’s perceptions and behaviors. Along these lines, we manipulated procedural search tasks along two orthogonal dimensions: product and goal. The product dimension relates to the main outcome of the task and the goal dimension relates to task’s success criteria. We manipulated tasks across three product categories and two goal categories. The study investigated four research questions. First, we examined the effects of the product and goal on participants’ (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. Second, regardless of the task product and goal, by analyzing participants’ think-aloud comments and screen activities we closely examined how people search for procedural knowledge. Specifically, we report on (RQ4) important relevance criteria, types of information sought, and challenges.

REFERENCES

  1. [1] Alemu Eyob N. and Huang Jianbin. 2020. HealthAid: Extracting domain targeted high precision procedural knowledge from on-line communities. Information Processing & Management 57, 6 (2020). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Anderson Lorin W., Krathwohl David R., Airasian Peter W., Cruikshank Kathleen A., Mayer Richard E., Pintrich Paul R., Raths James, and Wittrock Merlin C.. 2001. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Complete Edition.Google ScholarGoogle Scholar
  3. [3] Bailey Peter and Jiang Li. 2012. User task understanding: A web search engine perspective. (2012). https://www.microsoft.com/en-us/research/publication/user-task-understanding-a-web-search-engine-perspective/Presentation delivered at the NII Shonan: Whole-Session Evaluation of Interactive Information Retrieval Systems workshop. 8-11 October 2012, Shonan, Japan.Google ScholarGoogle Scholar
  4. [4] Balatsoukas Panos and Ruthven Ian. 2012. An eye-tracking approach to the analysis of relevance judgments on the Web: The case of Google search engine. Journal of the American Society for Information Science and Technology 63, 9 (2012), 17281746.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Barry Carol L.. 1994. User-defined relevance criteria: An exploratory study. Journal of the American Society for Information Science 45, 3 (1994), 149159.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Byström Katriina and Järvelin Kalervo. 1995. Task complexity affects information seeking and use. Information Processing and Management 31, 2 (1995), 191213.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Campbell Donald J.. 1988. Task complexity: A review and analysis. The Academy of Management Review 13, 1 (1988), 4052.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Capra Robert and Arguello Jaime. 2023. How does AI chat change search behaviors? arXiv (2023). https://arxiv.org/abs/2307.03826Google ScholarGoogle Scholar
  9. [9] Capra Robert, Arguello Jaime, Crescenzi Anita, and Vardell Emily. 2015. Differences in the use of search assistance for tasks of varying complexity. Association for Computing Machinery, New York, NY, USA, 2332.Google ScholarGoogle Scholar
  10. [10] Cascio M. Ariel, Lee Eunlye, Vaudrin Nicole, and Freedman Darcy A.. 2019. A team-based approach to open coding: Considerations for creating intercoder consensus. Field Methods 31, 2 (2019), 116130.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Choi Bogeum and Arguello Jaime. 2020. A qualitative analysis of the effects of task complexity on the functional role of information. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 328332.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Choi Bogeum, Arguello Jaime, and Capra Robert. 2023. Understanding procedural search tasks “in the wild”. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR’23). Association for Computing Machinery, New York, NY, USA, 2433. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Choi Bogeum, Casteel Sarah, Capra Robert, and Arguello Jaime. 2022. Procedural knowledge search by intelligence analysts(CHIIR ’22). Association for Computing Machinery, New York, NY, USA, 169179.Google ScholarGoogle Scholar
  14. [14] Choi Bogeum, Ward Austin, Li Yuan, Arguello Jaime, and Capra Robert. 2019. The effects of task complexity on the use of different types of information in a search assistance tool. ACM Trans. Inf. Syst. 38, 1, Article 9 (Dec2019), 28 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Chu Cuong Xuan, Tandon Niket, and Weikum Gerhard. 2017. Distilling task knowledge from how-to communities. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17). International World Wide Web Conferences Steering Committee, 805814. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Cole Michael J., Gwizdka Jacek, Liu Chang, Bierig Ralf, Belkin Nicholas J., and Zhang Xiangmin. 2011. Task and user effects on reading patterns in information search. Interacting with Computers 23, 4 (052011), 346362. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Crystal Abe and Greenberg Jane. 2006. Relevance criteria identified by health information users during Web searches. Journal of the American Society for Information Science and Technology 57, 10 (2006), 13681382.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Eickhoff Carsten, Teevan Jaime, White Ryen, and Dumais Susan. 2014. Lessons from the Journey: A Query Log Analysis of Within-Session Learning. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM ’14). ACM, New York, NY, USA, 223232. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Eiriksdottir Elsa and Catrambone Richard. 2011. Procedural instructions, principles, and examples: How to structure instructions for procedural tasks to enhance performance, learning, and transfer. Human Factors 53, 6 (2011), 749770.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Elo Satu and Kyngäs Helvi. 2008. The qualitative content analysis process. Journal of Advanced Nursing 62, 1 (2008), 107115.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Ertl Bernhard. 2009. Conceptual and procedural knowledge construction in computer supported collaborative learning. In Proceedings of the 9th International Conference on Computer Supported Collaborative Learning (CSCL’09). International Society of the Learning Sciences, 137141.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Freund Luanne, Toms Elaine G., and Waterhouse Julie. 2005. Modeling the information behaviour of software engineers using a work-task framework. Proceedings of the American Society for Information Science and Technology (2005).Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Frummet Alexander, Elsweiler David, and Ludwig Bernd. 2022. “What can I cook with these ingredients?” — understanding cooking-related information needs in conversational search. ACM Transactions of Information Systems 40, 4, Article 81 (2022).Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Georgeff M. P. and Lansky A. L.. 1986. Procedural knowledge. Proc. IEEE 74, 10 (1986), 13831398. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] González-Betancor Sara M., Bolívar-Cruz Alicia, and Verano-Tacoronte Domingo. 2019. Self-assessment accuracy in higher education: The influence of gender and performance of university students. Active Learning in Higher Education 20, 2 (July2019), 101114. . Publisher: SAGE Publications.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Kelly Diane, Arguello Jaime, Edwards Ashlee, and Wu Wan-ching. 2015. Development and evaluation of search tasks for IIR experiments using a cognitive complexity framework. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval (ICTIR ’15). Association for Computing Machinery, New York, NY, USA, 101110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Kyngäs Helvi. 2020. Inductive content analysis. The Application of Content Analysis in Nursing Science Research (2020), 1321.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] LeFevre Jo-Anne and Dixon Peter. 1986. Do written instructions need examples? Cognition and Instruction 3, 1 (1986), 130.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Li Yuelin and Belkin Nicholas J.. 2008. A faceted approach to conceptualizing tasks in information seeking. Information Processing & Management 44, 6 (2008), 18221837.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Liu Jingjing, Cole Michael J., Liu Chang, Bierig Ralf, Gwizdka Jacek, Belkin Nicholas J., Zhang Jun, and Zhang Xiangmin. 2010. Search behaviors in different task types. In Proceedings of the 10th Annual Joint Conference on Digital Libraries (JCDL ’10). Association for Computing Machinery, New York, NY, USA, 6978.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Liu Jingjing, Liu Chang, and Belkin Nicholas. 2013. Examining the effects of task topic familiarity on searchers’ behaviors in different task types. In Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries (ASIST ’13). American Society for Information Science, USA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Michas Irene C. and Berry Dianne C.. 2000. Learning a procedural task: Effectiveness of multimedia presentations. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition 14, 6 (2000), 555575.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Mujtaba Dena and Mahapatra Nihar. 2019. Recent trends in natural language understanding for procedural knowledge. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). 420424. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Pardi Georg, Kammerer Yvonne, and Gerjets Peter. 2019. Search and justification behavior during multimedia web search for procedural knowledge. In Companion Publication of the 10th ACM Conference on Web Science (WebSci ’19). ACM, New York, NY, USA, 1720.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Park Hogun and Nezhad Hamid Reza Motahari. 2018. Learning procedures from text: Codifying how-to procedures in deep neural networks. In Companion Proceedings of The Web Conference 2018 (WWW ’18). International World Wide Web Conferences Steering Committee, 351358. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Pothirattanachaikul Suppanut, Yamamoto Takehiro, Fujita Sumio, Tajima Akira, and Tanaka Katsumi. 2017. Mining alternative actions from community Q&A corpus for task-oriented web search. In Proceedings of the International Conference on Web Intelligence (WI ’17). ACM, New York, NY, USA, 607614. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Reyes Victoria, Bogumil Elizabeth, and Welch Levin Elias. 2021. The living codebook: Documenting the process of qualitative data analysis. Sociological Methods & Research (2021), 0049124120986185.Google ScholarGoogle Scholar
  38. [38] Savolainen Reijo and Kari Jarkko. 2006. User-defined relevance criteria in web searching. Journal of Documentation (2006).Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Schneider Michael and Stern Elsbeth. 2010. The developmental relations between conceptual and procedural knowledge: A multimethod approach. Developmental Psychology 46, 1 (2010), 178.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Schumacher Pol, Minor Mirjam, Walter Kirstin, and Bergmann Ralph. 2012. Extraction of procedural knowledge from the web: A comparison of two workflow extraction approaches. In Proceedings of the 21st International Conference on World Wide Web (WWW ’12 Companion). ACM, New York, NY, USA, 739747. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Smith Eliot R.. 1994. Procedural knowledge and processing strategies in social cognition. Handbook of Social Cognition 2 (1994), 99152.Google ScholarGoogle Scholar
  42. [42] Taylor Arthur. 2012. User relevance criteria choices and the information search process. Information Processing & Management 48, 1 (2012), 136153.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Berge Timon ten and Hezewijk Rene van. 1999. Procedural and declarative knowledge: An evolutionary perspective. Theory & Psychology 9, 5 (1999), 605624. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Berge Timon Ten and Hezewijk René Van. 1999. Procedural and declarative knowledge: An evolutionary perspective. Theory & Psychology 9, 5 (1999), 605624.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Urgo Kelsey and Arguello Jaime. 2022. Understanding the “pathway” towards a searcher’s learning objective. ACM Transactions of Information Systems 40, 4 (2022).Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. [46] Urgo Kelsey, Arguello Jaime, and Capra Robert. 2020. The effects of learning objectives on searchers’ perceptions and behaviors(ICTIR ’20). ACM, New York, NY, USA, 7784. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Völske Michael, Braslavski Pavel, Hagen Matthias, Lezina Galina, and Stein Benno. 2015. What users ask a search engine: Analyzing one billion Russian question queries. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM ’15). ACM, New York, NY, USA, 15711580. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Weber Ingmar, Ukkonen Antti, and Gionis Aris. 2012. Answers, not links: Extracting tips from yahoo! answers to address how-to web queries. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM ’12). ACM, New York, NY, USA, 613622. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Wildemuth Barbara, Toms Elaine G., and Freund Luanne. 2014. Untangling search task complexity and difficulty in the context of interactive information retrieval studies. Journal of Documentation 70, 6 (Oct.2014), 11181140.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Wynder Monte B. and Luckett Peter F.. 1999. The effects of understanding rules and a worked example on the acquisition of procedural knowledge and task performance. Accounting & Finance 39, 2 (1999), 177203.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Xu Yunjie and Chen Zhiwei. 2006. Relevance judgment: What do information users consider beyond topicality? Journal of the American Society for Information Science and Technology 57, 7 (2006), 961973.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Yang Zi and Nyberg Eric. 2015. Leveraging procedural knowledge for task-oriented search(SIGIR ’15). ACM, New York, NY, USA, 513522. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Zhang Ziqi, Webster Philip, Uren Victoria, Varga Andrea, and Ciravegna Fabio. 2012. Automatically extracting procedural knowledge from instructional texts using natural language processing. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC ’12). European Language Resources Association (ELRA), Istanbul, Turkey, 520527.Google ScholarGoogle Scholar

Index Terms

  1. Better Understanding Procedural Search Tasks: Perceptions, Behaviors, and Challenges

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Information Systems
      ACM Transactions on Information Systems  Volume 42, Issue 3
      May 2024
      721 pages
      ISSN:1046-8188
      EISSN:1558-2868
      DOI:10.1145/3618081
      Issue’s Table of Contents

      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].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 December 2023
      • Online AM: 23 October 2023
      • Accepted: 5 October 2023
      • Revised: 23 September 2023
      • Received: 27 April 2023
      Published in tois Volume 42, Issue 3

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
    • Article Metrics

      • Downloads (Last 12 months)177
      • Downloads (Last 6 weeks)22

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text