Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

In this chapter, situations that can cause split of attention in multimedia environments were determined via eye tracking method. Fixation numbers, heat maps and area of interest of learners were analyzed. As a result of these analyses, design suggestions were determined for multimedia environments to provide focusing attention to content without split attention effect. Visual and auditory resources should be provided simultaneously. Visual information should be supported with auditory expression instead of texts. Images such as videos, pictures and texts should not be presented on the same screen. Texts provided with pictures should be presented via integration to each other instead of separate presentation of text and picture. Texts provided with videos should be presented via integration to each other instead of separate presentation of text and video. Images should be given via marking important points on images to increase attention.

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Eye Tracking Applications for E-Learning Purposes

Cognitive Computing in Technology-Enhanced Learning - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-9031-6.ch007 ◽

2019 ◽

pp. 151-174

Author(s):

Ismail El Haddioui

Keyword(s):

Eye Movements ◽

Eye Tracking ◽

Learning Environments ◽

Movement Analysis ◽

Online Behavior ◽

Eye Movement Analysis ◽

Child Education ◽

E Learning ◽

Learning Platforms ◽

Customized Learning

E-learning has become a fundamental part of child education, higher education, and corporate training. In the design of adaptive e-learning environments, it is important to track and analyze learner behavior and preferences, and this is possible by recording their eye movements. Eye tracking is a technology developed to monitor eye movements allowing us to analyze the recorded gaze data. The main goal of this chapter is to determine the potential of eye tracking in the field of e-learning and the various applications of eye movement analysis for e-learning platforms. Results can be used to design an adaptive e-learning environment able to collect, analyze, and understand learner online behavior, preferences, and needs, and then offer an educational content adapted to each learner's needs by generating new customized learning situations.

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Detecting Online Learners' Reading Ability via Eye-Tracking

Optimizing Student Engagement in Online Learning Environments - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-3634-5.ch008 ◽

2018 ◽

pp. 163-185

Author(s):

Samiullah Paracha ◽

Ayaka Inuoue ◽

Sania Jehanzeb

Keyword(s):

Eye Tracking ◽

Learning Environments ◽

Reading Ability ◽

Short Term Memory ◽

Reading Performance ◽

Accurate Assessment ◽

Online Learners ◽

Motivation To Read ◽

Tracking Method ◽

Instructional Goal

Nurturing the motivation to read is an important instructional goal. There can be a number of reasons for a learner to have problems with reading in online learning environments: (1) eyes being unable to scan easily along a line of print; or (2) as a result of concentrating on controlling the eyes concentration, the short-term memory become impaired. The study reported in this chapter used eye tracking method to provide a useful experimental design for exploring reading performance of university online learners. Different eye-tracking experiments were carried out to help informing the teachers to improve the learning environment and be able to do more accurate assessment about what the students were attending to on the screen.

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