5 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
Factuality Checking in News Headlines with Eye Tracking
We study whether it is possible to infer if a news headline is true or false
using only the movement of the human eyes when reading news headlines. Our
study with 55 participants who are eye-tracked when reading 108 news headlines
(72 true, 36 false) shows that false headlines receive statistically
significantly less visual attention than true headlines. We further build an
ensemble learner that predicts news headline factuality using only eye-tracking
measurements. Our model yields a mean AUC of 0.688 and is better at detecting
false than true headlines. Through a model analysis, we find that eye-tracking
25 users when reading 3-6 headlines is sufficient for our ensemble learner.Comment: Accepted to SIGIR 202
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)
Models and Algorithms for Understanding and Supporting Learning Goals in Information Retrieval
While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one, possibly involving unnecessarily difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. My work addresses these problems through multiple studies where various models and frameworks are developed and tested to support particular dimensions of search as learning. Empirical analysis of these studies through user studies demonstrate promising results and provide a solid foundation for further work.
The earliest work we focused on centered on developing a framework and algorithms to support vocabulary learning objectives in a Web document context. The proposed framework incorporates user information, topic information and effort constraints to provide a desirable combination of personalized and efficient (by word length) learning experience. Our user studies demonstrate the effectiveness of our framework against a strong commercial baseline's (Google search) results in both short- and long-term assessment.
While topic-specific content features (such as frequency of subtopic occurrences) naturally play a role in influencing learning outcomes, stylistic and structural features of the documents themselves may also play a role. Using such features we construct robust regression models that show strong predictive strength for multiple measures of learning outcomes. We also show early evidence that regression models trained on one dataset of search as learning can show strong test-set predictions on an independent dataset of search as learning, suggesting a certain degree of generalizability of stylistic content features.
The models developed in my work are designed to be as generalizable, scalable and efficient as possible to make it easier for practitioners in the field to improve how people use search engines for learning. Finally, we investigate how gaze-tracking and automatic question generation could be used to scale a form of active learning to arbitrary text material. Our results show promising potential for incorporating interactive learning experiences in arbitrary text documents on the Web. A major theme in these studies centers on understanding and improving how people learn when using Web search engines. We also put specific emphasis on long-term learning outcomes and demonstrate that our models and frameworks actually yield sustainable knowledge gains, both for passive and interactive learning. Taken together, these research studies provide a solid foundation for multiple promising directions in exploring search as learning.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155065/1/rmsyed_1.pd