EEG Cognition Detection to Support Aptitude-Treatment Interaction in E-Learning Platforms

Mwambe, Othmar Othmar; Kamioka, Eiji

doi:10.1109/seatuc.2018.8788854

Cited by 6 publications

(23 citation statements)

References 10 publications

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“…The cognitive process referred to in this study is learner's intrinsic motivation states during the learning process. With respect to this study hypothesis described in Section 1, learner's motivation state alters when experiencing work overload (difficult learning) [22,26,27]. Hence, this study approach utilizes such alteration of learner's motivation states to initiate navigation support process which supports AEHS adaptive decision-making process.…”

Section: Background Informationmentioning

confidence: 99%

“…Knowledge levels in most AEHS are predefined when setting up learner's profile to reflect learner's prior-knowledge. However, dynamic nature of learners' understanding is influenced by several other cognitive factors such as working memory [26] which creates challenge in determining precise time to initiate support and what type of support learner needs. Thus, some recent studies focus on a biological-based approach to detect such cognitive state variation trends [26] and dynamic knowledge profile updates [16].…”

Section: Adaptive Educational Hypermedia Systemsmentioning

confidence: 99%

“…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]. Hence, the design of this study approach is highly influenced by the previously mentioned pilot studies on Brain-Computer Interface (BCI) [23][24][25][26] and cognitive load [22,27].…”

Section: Introductionmentioning

confidence: 99%

“…Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27]. Hence, the design of this study approach is highly influenced by the previously mentioned pilot studies on Brain-Computer Interface (BCI) [23][24][25][26] and cognitive load [22,27]. Thus, this study approach derives its conceptual framework not only from educational technology (AEHS) [28] but also cognitive science (visual spatial attention and cognitive load) and educational psychology.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this study is to reduce time imposed by navigation links and work overload so as to increase learners' motivation by supporting AEHS adaptive-decision process. To achieve such objective, this study proposes a bioinformatics-based adaptive navigation support as an extension of our previous studies on utilization of learners' metacognitive experiences [22,26]. This study hypothesizes that as long as metacognitive experiences influence learners' cognitive processes and learning styles during the learning process, then such cognitive states' alteration can be utilized to support real-time integration of learners' dynamic learning styles (LS) with LO models in AEHS and obtain timely accurate information for an adaptive decision-making process.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Background Informationmentioning

confidence: 99%

Section: Adaptive Educational Hypermedia Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

Self Cite

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Deployment of information processing theory to support adaptive e-learning systems: Feasibility study

Mwambe

2024

Computers in Human Behavior Reports

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Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

Jamil

Belkacem²,

Ouhbi

et al. 2021

IEEE Access

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Brain-computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching-learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students' learning and cognitive skills-when consistently associated with the different pedagogical teaching-learning strategies-for better academic achievement.INDEX TERMS 21st century abilities, Applications in subject areas, Brain computer interface, Improving classroom teaching, Neurofeedback, Teaching/learning strategies.

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EEG Cognition Detection to Support Aptitude-Treatment Interaction in E-Learning Platforms

Cited by 6 publications

References 10 publications

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

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

Deployment of information processing theory to support adaptive e-learning systems: Feasibility study

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

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