Date of Award
2017
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Kalva, Hari
Abstract
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.
Extent
158 p.
Recommended Citation
Parikh, Saurin Sharad, "Predicting Levels of Learning with Eye Tracking" (2017). Electronic Theses and Dissertations 2010 - 2019. 1761.
https://digitalcommons.fau.edu/etd_general_6/1761
Identifier
FA00005941
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2017.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.