Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

Abstract

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS' page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners' preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners' learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners' motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners' pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners' performance up to 78%.

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

“…When learners experience difficult learning (work overload) their attention levels reflect motivation states [3,5]. Hence, alteration of such cognitive processes can not only be utilized to support AEHS adaptation process [5,6], but also influence their learning efficacy [7].…”

“…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].…”

“…Sci. 2021, 11, 49 2 of 13 size dilation and reading time when using AEHS has been explored so as to enable cognitive load estimation in support of AEHS adaptive process as an extension of the previously proposed bioinformatics-based approach [6]. This study result has found correlation between cognitive load and learners' endogenous blinking rate; hence, AEHS adaptive process algorithm has been updated.…”

“…In our previous studies [3,5], we carefully investigated the possibility of enrolling such cognitive processes into e-learning platforms and proposed an adaptive algorithm that can support AEHS adaptive decision-making process [6]. Therefore, as an extension of such studies [3,5,6], in this study correlation among learner's endogenous blinking rate, pupil Educ. Sci.…”

“…[3]. Hence, few recent research studies have aggressively started to take cognitive computing approach towards such cognitive processes [3][4][5][6], so as to enable AEHS adaptation process to attain productive human-automation interaction.…”