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Predicting Levels of Learning with Eye Tracking

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Date Issued:
2017
Summary:
E-Learning is transforming the delivery of education. Today, millions of students take selfpaced online courses. However, the content and language complexity often hinders comprehension, and that with lack of immediate help from the instructor leads to weaker learning outcomes. Ability to predict difficult content in real time enables eLearning systems to adapt content as per students' level of learning. The recent introduction of lowcost eye trackers has opened a new class of applications based on eye response. Eye tracking devices can record eye response on the visual element or concept in real time. The response and the variations in eye response to the same concept over time may be indicative of the levels of learning. In this study, we have analyzed reading patterns using eye tracker and derived 12 eye response features based on psycholinguistics, contextual information processing, anticipatory behavior analysis, recurrence fixation analysis, and pupils' response. We use eye responses to predict the level of learning for a term/concept. One of the main contribution is the spatio-temporal analysis of the eye response on a term/concept to derive relevant first pass (spatial) and reanalysis (temporal) eye response features. A spatiotemporal model, built using these derived features, analyses slide images, extracts words (terms), maps the subject's eye response to words, and prepares a term-response map. A parametric baseline classifier, trained with labeled data (term-response maps) classifies a term/concept as a novel (positive class) or familiar (negative class), using majority voting method. On using, only first pass features for prediction, the baseline classifier shows 61% prediction accuracy, but on adding reanalysis features, baseline achieves 66.92% accuracy for predicting difficult terms. However, all proposed features do not have the same response to learning difficulties for all subjects, as we consider reading as an individual characteristic. Hence, we developed a non-parametric, feature weighted linguistics classifier (FWLC), which assigns weight to features based on their relevance. The FWLC classifier achieves a prediction accuracy of 90.54% an increase of 23.62% over baseline and 29.54% over the first-pass variant of baseline. Predicting novel terms as familiar is more expensive because content adapts by using this information. Hence, our primary goal is to increase the prediction rate of novel terms by minimizing the cost of false predictions. On comparing the performance of FWLC with other frequently used machine learning classifiers, FWLC achieves highest true positive rate (TPR) and lowest ratio of false negative rate (FNR) to false positive rate (FPR). The higher prediction performance of proposed spatio-temporal eye response model to predict levels of learning builds a strong foundation for eye response driven adaptive e-Learning.
Title: Predicting Levels of Learning with Eye Tracking.
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Name(s): Parikh, Saurin Sharad, author
Kalva, Hari, Thesis advisor
Florida Atlantic University, Degree grantor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2017
Date Issued: 2017
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 158 p.
Language(s): English
Summary: E-Learning is transforming the delivery of education. Today, millions of students take selfpaced online courses. However, the content and language complexity often hinders comprehension, and that with lack of immediate help from the instructor leads to weaker learning outcomes. Ability to predict difficult content in real time enables eLearning systems to adapt content as per students' level of learning. The recent introduction of lowcost eye trackers has opened a new class of applications based on eye response. Eye tracking devices can record eye response on the visual element or concept in real time. The response and the variations in eye response to the same concept over time may be indicative of the levels of learning. In this study, we have analyzed reading patterns using eye tracker and derived 12 eye response features based on psycholinguistics, contextual information processing, anticipatory behavior analysis, recurrence fixation analysis, and pupils' response. We use eye responses to predict the level of learning for a term/concept. One of the main contribution is the spatio-temporal analysis of the eye response on a term/concept to derive relevant first pass (spatial) and reanalysis (temporal) eye response features. A spatiotemporal model, built using these derived features, analyses slide images, extracts words (terms), maps the subject's eye response to words, and prepares a term-response map. A parametric baseline classifier, trained with labeled data (term-response maps) classifies a term/concept as a novel (positive class) or familiar (negative class), using majority voting method. On using, only first pass features for prediction, the baseline classifier shows 61% prediction accuracy, but on adding reanalysis features, baseline achieves 66.92% accuracy for predicting difficult terms. However, all proposed features do not have the same response to learning difficulties for all subjects, as we consider reading as an individual characteristic. Hence, we developed a non-parametric, feature weighted linguistics classifier (FWLC), which assigns weight to features based on their relevance. The FWLC classifier achieves a prediction accuracy of 90.54% an increase of 23.62% over baseline and 29.54% over the first-pass variant of baseline. Predicting novel terms as familiar is more expensive because content adapts by using this information. Hence, our primary goal is to increase the prediction rate of novel terms by minimizing the cost of false predictions. On comparing the performance of FWLC with other frequently used machine learning classifiers, FWLC achieves highest true positive rate (TPR) and lowest ratio of false negative rate (FNR) to false positive rate (FPR). The higher prediction performance of proposed spatio-temporal eye response model to predict levels of learning builds a strong foundation for eye response driven adaptive e-Learning.
Identifier: FA00005941 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2017.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Dissertations, Academic -- Florida Atlantic University
Eye tracking.
E-Learning.
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00005941
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.