How user information search patterns impact learning outcomes across different stages of learning-related search tasks

Abstract

As information search increasingly becomes a means of knowledge acquisition, improving user learning outcomes has become critical for users and search engines. To identify search patterns that lead to high learning outcomes, fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze data collected from a lab experiment. Six search patterns that optimize learning were proposed: active engagement, time-intensive exploration, sophisticated and efficient, simple and efficient, extensive querying, and comprehensive browsing. Users dynamically adopt these search patterns depending on their task cognitive level (receptive and critical) and search stage (early, middle, and late). By examining how interactions among variables affect learning outcomes, the limitations of traditional variance- and regression-based methods that examine the independent effects of single factors were overcome. This research pioneers the use of fsQCA in search as learning (SAL) studies, provides novel insights into the interplay of search behaviors and uncovers multiple configurational pathways to learning success.