Eye Tracking Applications for E-Learning Purposes

Abstract

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.

Endogenous Eye Blinking Rate to Support Human–Automation Interaction for E-Learning Multimedia Content Specification

“…Hence, some recent studies have been exploring possibilities of using mobile sensors to measure cognitive load [3,5,7], so as to be able to predict learner's performance [7] and suitable adjustment for multimedia content personalization [4,6] on real-time basis. EEG [7,8] and eye-tracking [4,9] sensors have commonly been used for cognitive load measurements [3] and identification of the learner's area of interest (AOI) on multimedia content [4,6]. Eye tracking sensors have been found to be suitable as they can easily be embedded into systems and are more resilient to noise environment [6,10,11].…”

“…Eye tracking sensors have been found to be suitable as they can easily be embedded into systems and are more resilient to noise environment [6,10,11]. Commonly known parameters for cognitive load measurement in Eye-tracking are pupil size dilation and endogenous blinking rate [9,10]. Real-time cognitive load estimation in multimedia environment is very crucial as self-rating measurements which has been widely used by many tutoring systems cannot capture learners' cognitive processes alternation [10].…”

“…Eye-tracking has commonly been used to investigate relation between visual attention and multimedia content [20]. Pupil size dilation, fixation duration and blink frequency are eye-tracking key parameters associated with measurement of learners "cognitive load" [9,21], and thus, such eye-tracking parameters have the advantage of providing easy implementation. Therefore, considering the demand for suitable cognitive load-measurement traits that can support the AEHS adaptive process, as stated in Section 1, such parameters are needed to enable the AEHS decision-making process to manage real-time cognitive load estimation, which has not yet been implemented [6].…”