Utilizing Eye Tracking to Improve Learning from Examples

Najar, Amir Shareghi; Mitrović, Antonija; Neshatian, Kourosh

doi:10.1007/978-3-319-07440-5_38

Cited by 16 publications

(4 citation statements)

References 17 publications

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“…Guarnera et al created the iTrace eyetracking infrastructure, which allows software engineers to work under more realistic conditions by enabling an eyetracking option in several integrated development environments [150]. Using eye-tracking data, Najar et al compared the behaviours of beginners and experienced students while studying examples to raise learning levels using an intelligent tutoring system [151].In a digital assessment game, videobased pupil monitoring eye-trackers were utilized to explore the allocation of the attention generated by the eye-tracker regarding an individual's locus of attention. In terms of meaand evaluating cognitive level, inspiring an interface design, discovering a behavioural response based on user data, improving teaching quality by improving the teaching framework, promoting the use of technology, and improving educational standards, eye-tracking technology has proven its efficiency and effectiveness.…”

Section: Educationmentioning

confidence: 99%

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

et al. 2022

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Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research.

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Section: Educationmentioning

confidence: 99%

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

et al. 2022

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“…Eye-tracking has the potential to enhance learning, for example through incorporation in intelligent learning technologies which make use of artificial intelligence which acts on data gathered from a user to alter the user experience in a personalised manner to optimise learning [1]. Although incorporation of eye tracking technology into such systems is still in its infancy, such technology has already been shown to, for example: improve attention during engagement with intelligent tutoring systems for learning Biology [2], Geography [3] and Computer Programming [4]; Respond to mind-wandering during reading [5]; Detect engagement in metacognitive processing [6]; Predict affect [7]. Particularly if future uses of such technology are aimed at reading-improvement, the software creators must be able to interpret the correspondence between eye-movement metrics and the type of reading the user is undergoing, validly.…”

Section: Introductionmentioning

confidence: 99%

Eye Movements during Barking at Print

Beelders¹,

Stott²

2020

Visual Impairment and Blindness - What We Know and What We Have to Know

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In order for educational software coupled with eye-tracking capability to respond with pedagogical appropriateness to a reader's eye movements, reading metrics must be validly interpreted. These metrics and the types of reading they diagnose, for example, scanning, skimming and reading for meaning, come largely from reading fiction texts in a home language. The use of existing classification systems for disadvantaged South African learners did not yield consistent and credible classification of these learners' reading. This could be attributed to learners barking at print, that is, decoding the text without comprehending what they were reading beyond the word level. Eye movements of barkers and non-barkers were analysed and no statistically significant differences were found. Barking at print was found to be distinct from mindless reading and mind-wandering, as well as other reading types for both first and second languages. Barking is characterised by slow reading with few regressions, average fixation durations typical of second language reading, and variability in eye-movements between lines of text. This work is significant in that it establishes that eye-movement during barking at print is distinct from other categories of reading. However, further research is needed before valid applications can be made from this work.

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“…For example, it is common in educational research not just to collect students' answers on the test, but also their entire interaction with the on-screen test, which results in a precise picture of students' behavior. Such additional data that include the time spent on a specific question or a change in the chosen answer may indicate that the student was in doubt (Najar et al, 2014). Furthermore, it is possible to track students' physiological measures, which may be combined with the aforementioned parameters to gain deeper insights into their knowledge and mental processes.…”

Section: Introductionmentioning

confidence: 99%

Determining Gaze Behavior Patterns in On-Screen Testing

Pejic

Savić

Segedinac

2020

Journal of Educational Computing Research

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This study proposes a software system for determining gaze patterns in on-screen testing. The system applies machine learning techniques to eye-movement data obtained from an eye-tracking device to categorize students according to their gaze behavior pattern while solving an on-screen test. These patterns are determined by converting eye movement coordinates into a sequence of regions of interest. The proposed software system extracts features from the sequence and performs clustering that groups students by their gaze pattern. To determine gaze patterns, the system contains components for communicating with an eye-tracking device, collecting and preprocessing students’ gaze data, and visualizing data using different presentation methods. This study presents a methodology to determine gaze patterns and the implementation details of the proposed software. The research was evaluated by determining the gaze patterns of 51 undergraduate students who took a general knowledge test containing 20 questions. This study aims to provide a software infrastructure that can use students’ gaze patterns as an additional indicator of their reading behaviors and their processing attention or difficulty, among other factors.

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Utilizing Eye Tracking to Improve Learning from Examples

Cited by 16 publications

References 17 publications

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

Eye Movements during Barking at Print

Determining Gaze Behavior Patterns in On-Screen Testing

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