  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.

Human-Computer Interaction