3 research outputs found
Relating Eye-Tracking Measures With Changes In Knowledge on Search Tasks
We conducted an eye-tracking study where 30 participants performed searches
on the web. We measured their topical knowledge before and after each task.
Their eye-fixations were labelled as "reading" or "scanning". The series of
reading fixations in a line, called "reading-sequences" were characterized by
their length in pixels, fixation duration, and the number of fixations making
up the sequence. We hypothesize that differences in knowledge-change of
participants are reflected in their eye-tracking measures related to reading.
Our results show that the participants with higher change in knowledge differ
significantly in terms of their total reading-sequence-length,
reading-sequence-duration, and number of reading fixations, when compared to
participants with lower knowledge-change.Comment: ACM Symposium on Eye Tracking Research and Applications (ETRA), June
14-17, 2018, Warsaw, Polan
Topic-independent modeling of user knowledge in informational search sessions
Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features. © 2021, The Author(s)