Improving prediction of user cognitive abilities and performance for user-adaptive narrative visualizations by leveraging eye-tracking data from multiple user studies
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Iranpour, Alireza
issued: 2021-10-21T21:23:25Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Previous work leveraged eye-tracking to predict a user’s levels of cognitive abilities and performance while reading magazine style narrative visualizations (MSNV), a common type of multimodal document which combines text and visualization to narrate a story. The eye-tracking data, used for training the classifiers, came from a user study, called control, where subjects simply read through MSNVs without receiving any type of adaptive guidance, otherwise known as the control condition. The goal was to capture the relationship between users’ normal MSNV processing and their levels of cognitive abilities and performance and use that to drive personalization. In addition to the control study, two other user studies were also previously conducted to investigate the benefits of adaptive support, also known as adaptive studies. In these studies, subjects were provided with gaze-based interventions to facilitate their processing of the MSNVs. In the control study, there was no intervention, and the MSNVs did not adapt to the users in any way because the idea was to make the predictions based on users normal, unguided MSNV processing and then use those predictions to deliver the appropriate adaptations. In the adaptive studies, however, interventions influenced the way subjects processed the MSNVs. As a result, their gaze behavior did not represent how they would have behaved in the intended control condition, and a classifier merely trained on the eye-tracking data from these studies would not learn the proper relationship. In this thesis, we propose different strategies for combining the additional eye-tracking data from the adaptive studies with our original data from the control study to mitigate the potential differences and to form more consistent combinations conducive to improved performance. Our results show that the additional eye-tracking data can significantly improve the accuracy of our classifiers.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/80022
note: Improving Prediction of User Cognitive Abilities and Performance for User-Adaptive Narrative Visualizations by Leveraging Eye-Tracking Data from Multiple User Studies by Alireza Iranpour B.Sc., Sharif University of Technology, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Computer Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2021 © Alireza Iranpour, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Improving Prediction of User Cognitive Abilities and Performance for User-Adaptive Narrative Visualizations by Leveraging Eye-Tracking Data from Multiple User Studies submitted by Alireza Iranpour in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Examining Committee: Cristina Conati, Computer Science, UBC Supervisor David Poole, Computer Science, UBC Supervisory Committee Member iii Abstract Previous work leveraged eye-tracking to predict a user’s levels of cognitive abilities and performance while reading magazine style narrative visualizations (MSNV), a common type of multimodal document which combines text and visualization to narrate a story. The eye-tracking data, used for training the classifiers, came from a user study, called control, where subjects simply read through MSNVs without receiving any type of adaptive guidance, otherwise known as the control condition. The goal was to capture the relationship between users’ normal MSNV processing and their levels of cognitive abilities and performance and use that to drive personalization. In addition to the control study, two other user studies were also previously conducted to investigate the benefits of adaptive support, also known as adaptive studies. In these studies, subjects were provided with gaze-based interventions to fa…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0402581
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Conati, Cristina
type: Text
identifier URI: http://hdl.handle.net/2429/80022