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Identification of information search strategies and their impact on learning outcome in learning‐related tasks
Author(s) -
Zhu Han,
Liu Chang,
Song Xiaoxuan
Publication year - 2018
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2018.14505501194
Subject(s) - outcome (game theory) , computer science , identification (biology) , selection (genetic algorithm) , cluster analysis , information retrieval , process (computing) , machine learning , botany , mathematics , mathematical economics , biology , operating system
This study aims to identify users' information search strategies in learning‐related tasks and investigates how the search strategies would influence their learning outcome. We conducted a lab‐based user experiment with 36 participants, recorded their search interactions during search process and measured their learning outcome by having them answer multiple choice questions after search. We extracted 14 behavioural features in three dimensions and conducted k‐means clustering analysis. Results showed that there were four types of search strategies: Economic Browsing, Supplementary Browsing, Exhaustive Browsing and Exhaustive Searching. It was found that Supplementary Browsing Strategy achieved the best learning outcome, while Economic Browsing Strategy showed the worst outcome, with Exhaustive Searching Strategy and Exhaustive Browsing Strategy in between. The findings indicated that careful selection of content pages and frequent querying were highly beneficial for users' learning during search. This study provides a deeper understanding of users' search strategies and has valuable implications on optimizing search systems to help users achieve better learning outcome.