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Users' Knowledge Use and Change during Information Searching Process: A Perspective of Vocabulary Usage

Published: 01 August 2020 Publication History

Abstract

One of the key questions in studies of Search as Learning is how to represent and measure the intangible and invisible learning processes that occur during the search process. In this study, participants are presented with two tasks and asked to represent their current relevant knowledge in a mind map. Participants then perform the search tasks and modify the mind maps as they search. In this paper we report on the use of measurement of vocabulary that is added to or removed from the mind map as a proxy for knowledge change in the search process. Mind maps represent user's knowledge with content in the tree structure. We examined the effect of users' prior knowledge upon their query behaviors by comparing users' pre-search mind map vocabulary with their query terms; and investigate how the content pages users read during search affected their knowledge change during search by comparing pre- and post-search mind maps. Our results demonstrated that users' prior knowledge played an important role in their query formulation. More than 50% of query terms came from users' prior knowledge, which accounted for nearly 40% of pre-search mind map vocabulary. As the search process proceeded, users added new vocabulary to their mind maps, and about 1/3 of new vocabulary was present in and copied directly from content pages. This study shows how prior knowledge and search results contribute to users' learning during information searching, and can help us better understand how learning occurred in the information searching process.

Supplementary Material

MP4 File (3383583.3398532.mp4)
This studied investigated the knowledge use and change during search process. Specifically, this paper examined the effect of users' prior knowledge on their query behaviors, and the effect of users' web page reading behaviors on their new knowledge acquisition. The results showed that user's knowledge structure played an important role in prior knowledge usage and new knowledge acquisition during information searching. Knowledge facets provide searching guidelines and query terms, while new knowledge mainly falls in knowledge points. Besides, task type and query order have influence on prior knowledge usage in the search process.

References

[1]
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390--412.
[2]
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1--48.
[3]
Belkin, N. J., Oddy, R. N., and Brooks, H. M. (1982). Ask for information retrieval: Part i. background and theory. Journal of documentation, 38(2), 61--71.
[4]
Bhattacharya, N., & Gwizdka, J. (2019). Measuring Learning During Search: Differences in Interactions, Eye-Gaze, and Semantic Similarity to Expert Knowledge. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. ACM, New York, NY, USA, 63--71.
[5]
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.
[6]
Cole, C. (1999). Activity of understanding a problem during interaction with an "enabling" information retrieval system: modeling information flow. Journal of the American Society for Information Science, 50(6), 544--552.
[7]
Cole, M. J., Gwizdka, J., Liu, C., Belkin, N. J., & Zhang, X. (2013). Inferring user knowledge level from eye movement patterns. Information Processing and Management, 49(5), 1075--1091.
[8]
Eickhoff, C., Teevan, J., White, R., & Dumais, S. (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, ACM, pp. 223--232.
[9]
Freund, L., Gwizdka, J., Hansen, P., Kando, N., &Rieh, S. Y. (2013). From searching to learning. In M. Agosti, N. Fuhr, E. Toms, & P. Vakkari (Eds.), Evaluation methodologies in information retrieval. Dagstuhl reports (Vol. 13441, pp. 102--105).
[10]
Freund, L., He, J., Gwizdka, J., Kando, N., Hansen, P., & Rieh, S. Y. (2014). Searching as learning (SAL) workshop. In Proceedings of the 5th information interaction in context symposium IIiX '14, ACM, pp.7.
[11]
Gadiraju, U., Yu, R. Dietze, S., and Holtz, P. (2018). Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. In Proceedings of the 2018 Conference on Human Information Interaction &Retrieval (CHIIR '18). ACM, New York, NY, USA, 2--11.
[12]
Ghosh,S., Rath,M. and Shah, C.(2018). Searching As Learning: Exploring Search Behavior and Learning Outcomes in Learning-Related Tasks. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval(CHIIR'18). ACM,New York, NY, USA, 22--31.
[13]
Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (2016). Search as learning (SAL) workshop. In Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, SIGIR '16, ACM, pp. 1249--1250.
[14]
Jansen, B. J., Spink, A., Bateman, J., & Saracevic, T. (1998). Real life information retrieval: a study of user queries on the Web. SIGIR Forum, 32(1), 5--17.
[15]
Lee, H., Lee, J., Makara, K. A., Fishman, B. J., & Hong, Y. (2015). Does higher education foster critical and creative learners? an exploration of two universities in south korea and the USA. Higher Education Research & Development, 34(1), 131--146.
[16]
Liu, C., & Song, X. (2018). How do information source selection strategies influence users' learning outcomes. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, ACM, pp. 257--260.
[17]
Liu, H., Liu, C., & Belkin, N. J. (2019). Investigation of users' knowledge change process in learning-related search tasks. Proceedings of the Association for Information Science and Technology, 56(1), 166--175.
[18]
Liu, J., Belkin, N. J., Zhang, X., & Yuan, X. (2013). Examining users' knowledge change in the task completion process. Information Processing & Management, 49(5), 1058--1074.
[19]
Lorin W. Anderson, David R. Krathwohl, and Benjamin Samuel Bloom. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. New York: Longman.
[20]
Lu, K., Joo, S., Lee, T., & Hu, R. (2017). Factors that influence query reformulations and search performance in health information retrieval: A multilevel modeling approach. Journal of the Association for Information Science and Technology, 68(8), 1886--1898.
[21]
Marchionini.G. (1997). Information Seeking in Electronic Environments. Cambridge University Press.
[22]
Rieh, S. Y., Collins-Thompson, K., Hansen, P., & Lee, H. J. (2016). Towards searching as a learning process. Journal of Information Science, 42(1), 19--34.
[23]
Song, X., Liu, C., & Liu, H. (2018). Characterizing and exploring users' task completion process at different stages in learning related tasks. Proceedings of the Association for Information Science and Technology, 55(1), 460--469.
[24]
Wacholder, N. (2010). Interactive query formulation. Annual Review of Information Systems and Technology, Vol. 45. Manuscript submitted for publication.
[25]
White, R. W. (2018). Opportunities and challenges in search interaction. Communications of the ACM, 61(12), 36--38.
[26]
White, R. W., Dumais, S. T, & Teevan, J. (2009). Characterizing the influence of domain expertise on web search behavior. In Proceedings of the second ACM international conference on web search and data mining, ACM, pp. 132--141.
[27]
Wildemuth, B. M. (2004). The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science and Technology, 55(3), 246--258.
[28]
Willoughby, T., Anderson, S. A., Wood, E., Mueller, J., & Ross, C. (2009). Fast searching for information on the internet to use in a learning context: the impact of domain knowledge. Computers & Education, 52(3), 640--648.
[29]
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., and Dietze, S. (2018). Predicting User Knowledge Gain in Informational Search Sessions. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, New York, NY, USA, 75--84.

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    cover image ACM Conferences
    JCDL '20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
    August 2020
    611 pages
    ISBN:9781450375856
    DOI:10.1145/3383583
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    Published: 01 August 2020

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    Author Tags

    1. information searching
    2. knowledge change
    3. query term
    4. search as learning
    5. vocabulary usage

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    • the National Social Science Fund of China

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    Cited By

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    • (2023)The Evolution of User Knowledge during Search-as-Learning Sessions: A Benchmark and BaselineProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578273(454-458)Online publication date: 19-Mar-2023
    • (2023)Navigational and thematic exploration–exploitation trade-offs during web search: effects of prior domain knowledge, search contexts and strategies on search outcomeBehaviour & Information Technology10.1080/0144929X.2023.224251443:10(2232-2258)Online publication date: 10-Aug-2023
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    • (2022)Relevance Models Based on the Knowledge GapAdvances in Information Retrieval10.1007/978-3-030-99739-7_60(488-495)Online publication date: 5-Apr-2022
    • (2021)Interest Development, Knowledge Learning, and Interactive IRProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446015(239-248)Online publication date: 14-Mar-2021

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