Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Akputu, Oryina Kingsley; Seng, Kah Phooi; Lee, Yunli; Ang, Li-Minn

doi:10.1145/3131287

Cited by 21 publications

(22 citation statements)

References 25 publications

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“…Therefore, the study suggests that endogenous blinking rate (BRT), LPsD and reading time (LRdT) are, together, reliable parameters to support cognitive load estimation and can also determine what extent AEHS adaptive process can support AEHS human-automation interaction. In addition, the study complies with previous studies in their demand for further investigation on additional bioinformatics parameters in order to support real-time cognitive load estimation [4,6,26] and the need for a better e-learning platform that can deliver learning content in consideration of these cognitive traits [3][4][5][6]25,27,28].…”

Section: Discussionsupporting

confidence: 69%

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

2021

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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.

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Section: Discussionsupporting

confidence: 69%

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

2021

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“…Thus, almost all the existing AEHS are formulated merely on the basis of two common pedagogical learning theories, namely constructivism and cognitivism, which do not fully support such learners' cognitive processes [8][9][10]. However, very few recent studies on AEHS have started exploring other pedagogical learning theories so as to adapt learners' cognitive processes [18][19][20][21] and metacognitive skills [22] into e-learning platforms. Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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%.

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“…About emotional data acquisition and collection, article [20] proposed a decision-making algorithm by which each sensor can decide on its acquired data so that event-insensitive data communication is reduced. The authors of article [21] explored the potential of utilizing affect or emotion recognition research in adaptive educational hypermedia models. Article [22] represented and depicted interrelationships between emotions and geographic locations.…”

Section: Related Workmentioning

confidence: 99%

Data collection scheme with minimum cost and location of emotional recognition edge devices

Jin

Qian

Chen

2019

Pers Ubiquit Comput

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This paper develops a real-time and reliable data collection system for big scale emotional recognition systems. Based on the data sample set collected in the initialization stage and by considering the dynamic migration of emotional recognition data, we design an adaptive Kth average device clustering algorithm for migration perception. We define a sub-modulus weight function, which minimizes the sum of the weights of the subsets covered by a cover to achieve high-precision device positioning. Combining the energy of the data collection devices and the energy of the wireless emotional device, we balance the data collection efficiency and energy consumption, and define a minimum access number problem based on energy and storage space constraints. By designing an approximate algorithm to solve the approximate minimum Steiner point problem, the continuous collection of emotional recognition data and the connectivity of data acquisition devices are guaranteed under the energy constraint of wireless devices. We validate the proposed algorithms through simulation experiments using different emotional recognition systems and different data scale. Furthermore, we analyze the proposed algorithms in terms of topology for devices classification, location accuracy, and data collection efficiency by comparing with the Bayesian classifier-based expectation maximization algorithm, the background difference-based moving target detection arithmetic averaging algorithm, and the Hungarian algorithm for solving the assignment problem.

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Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Cited by 21 publications

References 25 publications

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

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

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

Data collection scheme with minimum cost and location of emotional recognition edge devices

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