Abstract
As intelligent systems demand for human–automation interaction increases, the need for learners’ cognitive traits adaptation in adaptive educational hypermedia systems (AEHS) has dramatically increased. AEHS utilize learners’ cognitive processes to attain fair human–automation interaction for their adaptive processes. However, obtaining accurate cognitive trait for the AEHS adaptation process has been a challenge due to the fact that it is difficult to determine what extent such traits can comprehend system functionalities. Hence, this study has explored correlation among learners’ pupil size dilation, learners’ reading time and endogenous blinking rate when using AEHS so as to enable cognitive load estimation in support of AEHS adaptive process. An eye-tracking sensor was used and the study found correlation among learners’ pupil size dilation, reading time and learners’ endogenous blinking rate. Thus, the results show that endogenous blinking rate, pupil size and reading time are not only AEHS reliable parameters for cognitive load measurement but can also support human–automation interaction at large.
Highlights
Evolvement of human automation has paid profound effect on the development of intelligent systems that support adaptive learning
In adaptive educational hypermedia systems (AEHS), such function allocation is more complex as AEHS rely on learner’s cognitive traits to attain fair task distribution through adaptive process
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]
Summary
Evolvement of human automation has paid profound effect on the development of intelligent systems that support adaptive learning. Even though for decades human–automation interaction had been found to be effective and productive drive for the software systems development, less effort has been taken to explore to what extent human ability can comprehend such systems functionalities so as to attain fair functional tasks distribution between human and automation [2]. As an extension of such studies [3,5,6], in this study correlation among learner’s endogenous blinking rate, pupil 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].
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