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. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_39780 |
| Contributors | 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 |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 158 p., application/pdf |
| Rights | 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., http://rightsstatements.org/vocab/InC/1.0/ |
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