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Stopped yet Completed: Exploring the Relationships between Session-stopping Reasons, Information Types, and Cognitive Activities in Cross-Session Searches

Published: 10 March 2024 Publication History

Abstract

The aim of this study is to explore connections among the reasons that lead users to stop a search session, the types of information they found during the session, and the cognitive activities involved in interacting with such information – all in the context of complex tasks involving multiple search sessions. Prior research has primarily examined search-stopping behavior in single search sessions, focusing on instances when users identify needed information or are satisfied with their findings. However, recent insights from the Search as Learning (SAL) community highlight the relationships between user search behavior, the nature of the information they encountered, and various cognitive activities during the information-searching process. We conducted a diary study with 25 participants engaged in real-life tasks that spanned multiple search sessions over time. Our analysis found six predominant reasons users elected to stop a search session during the cross-session search process. We also examined the types of information that participants found and the cognitive activities that they engaged in during search sessions. We found statistically significant associations between the information types found and the session stopping reasons. We did not find significant associations between the cognitive activities and stopping reasons, but did observe that almost half the sessions reached the evaluate level of cognitive activity. Our findings provide insights about search behaviors and help inform the design of tools to support users working on multi-session search tasks.

References

[1]
Eugene Agichtein, Ryen W. White, Susan T. Dumais, and Paul N. Bennet. 2012. Search, Interrupted: Understanding and Predicting Search Task Continuation(SIGIR ’12). Association for Computing Machinery, New York, NY, USA, 315–324. https://doi.org/10.1145/2348283.2348328
[2]
Lorin W. Anderson and David R. Krathwohl. 2001. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman, New York.
[3]
Lorin W. Anderson and David R. Krathwohl. 2001. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn & Bacon, Boston, MA.
[4]
Peter Bailey, Liwei Chen, Scott Grosenick, Li Jiang, Yan Li, Paul Reinholdtsen, Charles Salada, Haidong Wang, and Sandy Wong. 2012. User task understanding: a web search engine perspective. In NII shonan meeting on whole-session evaluation of interactive information retrieval systems, Kanagawa, Japan.
[5]
Glenn J Browne and Mitzi G Pitts. 2004. Stopping rule use during information search in design problems. Organizational Behavior and Human Decision Processes 95, 2 (2004), 208–224.
[6]
Glenn J Browne, Mitzi G Pitts, and James C Wetherbe. 2007. Cognitive stopping rules for terminating information search in online tasks. MIS quarterly (2007), 89–104.
[7]
Muniram Budhu and Anita Coleman. 2002. The design and evaluation of interactivities in a digital library. D-Lib Magazine 8, 11 (2002).
[8]
Katriina Byström. 1999. Task complexity, information types and information sources: examination of relationships. Tampere University Press.
[9]
Katriina Byström. 2002. Information and information sources in tasks of varying complexity. Journal of the American Society for information Science and Technology 53, 7 (2002), 581–591.
[10]
Katriina Byström and Kalervo Järvelin. 1995. Task complexity affects information seeking and use. Information processing & management 31, 2 (1995), 191–213.
[11]
Bogeum Choi, Jaime Arguello, and Robert Capra. 2023. Understanding Procedural Search Tasks “in the Wild”. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. 24–33.
[12]
Bogeum Choi, Austin Ward, Yuan Li, Jaime Arguello, and Robert Capra. 2019. The Effects of Task Complexity on the Use of Different Types of Information in a Search Assistance Tool. ACM Transactions on Information Systems (TOIS) 38, 1 (2019), 1–28.
[13]
William S Cooper. 1968. Expected search length: A single measure of retrieval effectiveness based on the weak ordering action of retrieval systems. American documentation 19, 1 (1968), 30–41.
[14]
J Shane Culpepper, Fernando Diaz, and Mark D Smucker. 2018. Research frontiers in information retrieval: Report from the third strategic workshop on information retrieval in lorne (swirl 2018). In ACM SIGIR Forum, Vol. 52. ACM New York, NY, USA, 34–90.
[15]
Dedema and Chang Liu. 2019. Examination of online information search stopping behaviors and stopping rules by task type. Proceedings of the Association for Information Science and Technology 56, 1 (2019), 631–633.
[16]
Bernhard Ertl. 2009. Conceptual and procedural knowledge construction in computer supported collaborative learning. (2009).
[17]
Luanne Freund, Elaine G Toms, and Julie Waterhouse. 2005. Modeling the information behaviour of software engineers using a work-task framework. Proceedings of the American Society for Information Science and Technology 42, 1 (2005).
[18]
Shuguang Han, Zhen Yue, and Daqing He. 2015. Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions. ACM Trans. Inf. Syst. 33, 4, Article 16 (April 2015), 34 pages. https://doi.org/10.1145/2738036
[19]
Fereshte Ilani, Mohsen Nowkarizi, and Sholeh Arastoopoor. 2023. Analysis of the factors affecting information search stopping behavior: A systematic review. Journal of Librarianship and Information Science (2023), 09610006231157091.
[20]
Bernard J Jansen, Danielle Booth, and Brian Smith. 2009. Using the taxonomy of cognitive learning to model online searching. Information Processing & Management 45, 6 (2009), 643–663.
[21]
Diane Kelly, Jaime Arguello, Ashlee Edwards, and Wan-Ching Wu. 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. 101–110.
[22]
Diane Kelly, Jaime Arguello, Ashlee Edwards, and Wan-ching Wu. 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. New York, NY, USA, 101–110.
[23]
Alexander Kotov, Paul N Bennett, Ryen W White, Susan T Dumais, and Jaime Teevan. 2011. Modeling and analysis of cross-session search tasks. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 5–14.
[24]
Donald H Kraft and T Lee. 1979. Stopping rules and their effect on expected search length. Information Processing & Management 15, 1 (1979), 47–58.
[25]
Yuan Li, Austin R. Ward, and Rob Capra. 2021. An Analysis of Information Types and Cognitive Activities Involved in Cross-Session Search. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (Canberra ACT, Australia) (CHIIR ’21). Association for Computing Machinery, New York, NY, USA, 313–317. https://doi.org/10.1145/3406522.3446044
[26]
Shin-Jeng Lin and Nick Belkin. 2005. Validation of a model of information seeking over multiple search sessions. Journal of the American Society for Information Science and Technology 56, 4 (2005), 393–415. https://doi.org/10.1002/asi.20127
[27]
Jingjing Liu and Nicholas J. Belkin. 2010. Personalizing Information Retrieval for Multi-session Tasks: The Roles of Task Stage and Task Type. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Geneva, Switzerland) (SIGIR ’10). ACM, New York, NY, USA, 26–33. https://doi.org/10.1145/1835449.1835457
[28]
Bonnie MacKay and Carolyn Watters. 2008. Understanding and supporting multi-session web tasks. Proceedings of the American Society for Information Science and Technology 45, 1 (2008), 1–13.
[29]
Gary Marchionini. 1997. Information seeking in electronic environments. Number 9. Cambridge university press.
[30]
David Maxwell, Leif Azzopardi, Kalervo Järvelin, and Heikki Keskustalo. 2015. Searching and stopping: An analysis of stopping rules and strategies. In Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, 313–322.
[31]
Dan Morris, Meredith Ringel Morris, and Gina Venolia. 2008. SearchBar: A Search-centric Web History for Task Resumption and Information Re-finding. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Florence, Italy) (CHI ’08). ACM, New York, NY, USA, 1207–1216.
[32]
Kathryn Ritgerod Nickles, Shawn P Curley, and P George Benson. 1995. Judgment-based and reasonging-based stopping rules in decision making under uncertainty. Vol. 7285. University of Minnesota.
[33]
Miamaria Saastamoinen, Sanna Kumpulainen, Pertti Vakkari, and Kalervo Järvelin. 2013. Task complexity affects information use: a questionnaire study in city administration. (2013).
[34]
Abigail J. Sellen, Rachel Murphy, and Kate L. Shaw. 2002. How knowledge workers use the web. In Proceedings of the SIGCHI conference on Human factors in computing systems Changing our world, changing ourselves - CHI ’02.
[35]
Amanda Spink. 1996. Multiple search sessions model of end-user behavior: An exploratory study. Journal of the American Society for Information Science 47, 8 (1996), 603–609.
[36]
Kelsey Urgo and Jaime Arguello. 2023. Goal-setting in support of learning during search: An exploration of learning outcomes and searcher perceptions. Information Processing & Management 60, 2 (2023), 103158.
[37]
Kelsey Urgo, Jaime Arguello, and Rob Capra. 2020. The Effects of Learning Objectives on Searchers’ Perceptions and Behaviors. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 77–84.
[38]
Tung Vuong, Miamaria Saastamoinen, Giulio Jacucci, and Tuukka Ruotsalo. 2019. Understanding user behavior in naturalistic information search tasks. Journal of the Association for Information Science and Technology 00, 0 (2019), asi.24201. https://doi.org/10.1002/asi.24201
[39]
Yiwei Wang, Jiqun Liu, Soumik Mandal, and Chirag Shah. 2017. Search successes and failures in query segments and search tasks: A field study. Proceedings of the Association for Information Science and Technology 54, 1 (2017), 436–445.
[40]
Dan Wu, Jing Dong, Yuan Tang, and Rob Capra. 2019. Understanding task preparation and resumption behaviors in cross-device search. Journal of the Association for Information Science and Technology (2019).
[41]
Wan-Ching Wu and Diane Kelly. 2014. Online search stopping behaviors: An investigation of query abandonment and task stopping. Proceedings of the American Society for Information Science and Technology 51, 1 (2014), 1–10.
[42]
Yan Zhang. 2012. The impact of task complexity on people’s mental models of MedlinePlus. Information Processing & Management 48, 1 (2012), 107–119.
[43]
Yinglong Zhang and Rob Capra. 2019. Understanding How People use Search to Support their Everyday Creative Tasks. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. ACM, 153–162.
[44]
Yinglong Zhang, Rob Capra, and Yuan Li. 2020. An In-Situ Study of Information Needs in Design-Related Creative Projects. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (Vancouver BC, Canada) (CHIIR ’20). Association for Computing Machinery, New York, NY, USA, 113–123.

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            CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval
            March 2024
            481 pages
            ISBN:9798400704345
            DOI:10.1145/3627508
            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: 10 March 2024

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            Author Tags

            1. Cognitive Activity
            2. Cross-Session Search
            3. Information Types
            4. Session Stopping Reasons

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