ASSESSING EYE-TRACKING TECHNOLOGY
FOR LEARNING-STYLE DETECTION IN
ADAPTIVE GAME-BASED LEARNING
Tracey J. Mehigan
1
Ian Pitt
IDEAS Research Group,
Dept. of Computer Science, UCC
INTRODUCTION
Background
 Adaptive eLearning systems

Learning-styles
 Felder-Silverman LSM



FSILS Questionnaire
Eye-tracking & eye-tracking technologies
Detecting Global / Sequential learners
 Detecting Visual / Verbal learners


Potential for Mobile GBL

Eye-tracking for GBL in mobile environments
2
BACKGROUND




Measurement of learning-styles facilitates the
provision of adaptive content to specific learner
needs
Data traditionally gathered using questionnaires
Recently, user interaction with learning systems
has proven a valuable method of data gathering
for learning-styles
Eye-tracking technology could provide a better
method of user data collection for learning-style
analysis
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ADAPTIVE LEARNING SYSTEMS


User interaction with learning systems has proven a
valuable method of data gathering for learning-styles
A number of studies in recent years have looked at the
inference of learning-styles using the FelderSilverman model





Bayesian Networks (García et al)
Behaviour Patterns (Graf and Kinshuk)
Feed Forward Neural Networks (Villaverde et al)
Mouse movement patterns (Spada et al)
Accelerometer Interaction (Mehigan et al)
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LEARNING-STYLES & PERSONALITY
MODELS


One of the most analysed cognitive features in the development of
adaptive systems in eLearning environments
Learning-style models classify students according to where they
fit on a number of scales depending upon how they process and
receive information
Learning Style & Personality Models
Myer-Briggs Model
The Big Five Model
Felder-Silverman LSM
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FELDER-SILVERMAN LSM


The Felder-Silverman Learning-Style Model (Felder &
Silverman 1988) is a widely employed model for
inferring individual learning-styles
Based on four learning dimensions:




Active /Reflective
Sensitive /Intuitive
Global / Sequential
Visual / Verbal
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LEARNING STYLES QUESTIONNAIRE
HTTP://WWW.ENGR.NCSU.EDU/LEARNINGSTYLES/ILSWEB.HTML
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EYE –TRACKING
“Eye-tracking works by reflecting invisible infrared
light onto an eye, recording the reflection pattern
with a sensor system, and then calculating the
exact point of gaze using a geometrical model.
Once the point of gaze is determined, it can be
visualized and shown on a computer monitor”
(Tobii.com)
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EYE-TRACKING RESEARCH

Eye tracking technology is widely used in many disciplines

Commerce, learning difficulties, etc.

Studies have been conducted based on HCI, visual cognition
and web accessibility

Research has explored saccade velocity, blink rate and the
degree of eyelid openness for determination of user’s tiredness
level

This can complement other information gained by the system
through user behavioural patterns

Few examples of eye-tracking-based work in eLearning and
subsequently, game based learning

However, researchers at a UK university are developing
computer games which can be controlled by eye movements
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TOBII EYE-TRACKING TECHNOLOGY



Calibrate for the user’s
vision
System records the user’s
eye movements while s/he
observes the test scene(s)
Analyse the data on
individual and / or group
level
Gaze plots
Heat maps
Statistics

Can be based on Areas of
Interest (AOI’s)
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DETECTING GLOBAL / SEQUENTIAL
LEARNERS

Sequential learners would show a slower vertical
speed of eye movement between fixation points and a
longer focus time than their Global counterparts

10 Participants selected from Dept of Computer Science
UCC – reflecting a balanced sample of Global and
Sequential learners

2 Screens Presented to the User



A Learning Screen
A Task Screen
To ensure that the user gaze is following the screen
content and not the cursor arrow the mouse is only
used to move between screens.
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GLOBAL & SEQUENTIAL LEARNERS
GAZE PATTERNS & HEAT MAPS
Global
Sequential
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DETECTING VISUAL & VERBAL
LEARNERS


Visual learners (as defined in terms of the FelderSilverman LSM) will exhibit longer total time
(fixation) duration on visual learning content
(images/graphics)
Verbal learners will exhibit longer total time
(fixation) duration on textual learning content

10 Participants selected from Dept of Computer Science
UCC – reflecting a balanced sample of Visual and Verbal
learners

2 Screens Presented to the User



A learning screen
A task screen
To ensure that the user gaze is following the
screen content and not the cursor arrow the
mouse is only used to move between screens.
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VISUAL & VERBAL LEARNERS
GAZE PATTERNS & HEAT MAPS
Visual
Verbal
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MOBILE LEARNING

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Mobile learning decreases limitation of traditional
learning systems through the mobility of portable
devices
The incorporation of mobile devices provides an
opportunity for ubiquity and collaboration in
education
The importance of the mobile phone to teenage
identity and the development of social friendship
networks facilitates incorporation of mLearning
Mobile devices could be used to encourage young
people to learn in a beneficial way
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MOBILE GBL
Mobile learning decreases limitation of learning location
with the mobility of general portable devices


Mobile GBL is usually Situated and Ubiquitous
“We see a potential move away from immersive, game based
learning, represented in traditional eLearning simulation
systems, toward the advancement of situated mLearning
environments” (Dede)


Game based learning can also improve learning in
specialist areas where students become engaged in a
situated learning environment which occurs within the
game (Williamson et al )
Mobile GBL can also be incorporated in blended and
other learning environments
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EYE-TRACKING FOR MOBILE GBL



Potential to track user learning-styles through
avatar movement via eye-based interaction
The provision of adaptive systems based on the
Felder-Silverman model could potentially offer
students increased motivation to learn through
personalised content
Felder-Silverman’s model provides matching
teaching-styles to each learning-styles and
therefore content can be specifically tailored to
the needs of each individual learner
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EYE-TRACKING MOBILE GBL
ENVIRONMENTS




Eye-trackers are becoming smaller and less
cumbersome, potentially offering a new method
of mobile device interaction
Recently ‘Gaze Gesturing’ has emerged as a
means of controlling device interaction (Drewes
et al)
Tobii ‘Glasses’ offer the next generation of mobile
eye-tracker
Recent work conducted by Miluzzo et al
facilitates the use of forward facing mobile device
cameras for eye-tracing purposes
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CONCLUSION
Background
 Adaptive eLearning systems

Learning-styles
 Felder-Silverman LSM



FSILS Questionnaire
Eye-tracking & eye-tracking technologies
Detecting Global / Sequential learners
 Detecting Visual / Verbal learners


Potential for Mobile GBL

Eye-tracking for GBL in mobile environments
19
QUESTIONS??
COMMENTS!!
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