Understanding user search processes across varying cognitive levels
This work bridges the gap between search, learning, and user behavior, though it is incremental as it applies existing methods to new data.
The study investigated how varying cognitive learning levels affect online search processes and knowledge gain, finding that cognitive level significantly impacts search behavior and knowledge acquisition, with more complex tasks leading to more user interactions.
Web is often used for finding information and with a learning intention. In this thesis, we propose a study to investigate the process of learning online across varying cognitive learning levels using crowd-sourced participants. Our aim was to study the impact of cognitive learning levels on search as well as increase in knowledge. We present 150 participants with 6 search tasks for varying cognitive levels and collect user interactions and submitted answers as user data. We present quantitative analysis of user data which shows that the outcome for all cognitive levels is learning by quantifying it as calculated knowledge gain. Further, we also investigate the impact of cognitive learning level on user interaction and knowledge gain with the help of user data. We demonstrate that the cognitive learning level of search session has a significant impact on user's search behavior as well as on knowledge that is gained. Further, we establish a pattern in which the search behavior changes across cognitive learning levels where the least complex search task has minimum number of user interactions and most complex search task has maximum user interactions. With this observation, we were able to demonstrate a relation between a learner's search behavior and Krathwohl's revised Bloom's taxonomic structure of cognitive processes. The findings of this thesis intend to provide a significant work to bridge the relation between search, learning, and user.