Where do we look?

An investigation of attention and

learning with eye tracking

Patrick Hays& Sidney D’Mello

Why Eye Tracking?

• Common use of eye trackers is usability research

to evaluate interfaces

• Eye tracking may also help get a glimpse of

underlying cognitive processes

• The more a user (especially good students) looks

at an area, the more important that area is for

learning

Eye Tracking Background

• Saccades– Rapid eye movements

– Occur 3-4 times a second

• Fixations– Resting points of gaze

– Occur between saccades

Our Software: Guru Tutor

• Guru is an Intelligent Tutoring System, learning

software that speaks to the user via text-to-

speech

• Guru is designed to model expert Biology

teachers pedagogical strategies

• We are running this experiment with an alpha

version of the software

Guru (Early Alpha Build)

Guru and Eye Tracking

• Can we determine if a user was engaged

based on eye tracking data?

• Do people who learned more look at the

divisions of the screen differently?

Experiment

• 90 undergraduates at U of M

• Viewed 8 biology lectures taught by a computer tutor

• 3 Conditions– Monologue (no interaction with tutor agent)

– Dialogue (user types responses to tutor agent)

– Vicarious (user watches simulated student type responses to tutor agent)

• Pre-test and post-test

• Engagement measures throughout

• Areas of Interest (AOI’s) – i.e. logical divisions of the screen

• The AOI gets an “AOI hit” when the eye tracker detects the user’s gaze change to that AOI

Eye Tracking and Engagement

• AOI Hits for Image and Mean engagement– Monologue: r = -.083

– Dialogue: r = .569**

– Vicarious: r = .165

-0.083

0.569

0.165

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Monlogue Dialogue Vicarious

Corr

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Image AOI Hits and Engagement

Condition

• AOI Hits for Image and Learning gains– Monologue: r = .452*

– Dialogue: r = .587**

– Vicarious: r = .057

Eye Tracking and Learning

0.452

0.587

0.057

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Monlogue Dialogue Vicarious

Corr

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Image AOI Hits and Learning

Condition

Implications• Users with higher reported engagement and

higher learning performance spend more time looking at the image AOI

• Implications for tutoring systems: design rich, high quality images to improve engagement

• Dialogue condition seems to the preferred interaction style for engaged and high performing students

Thanks for listening!