Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Balamurugan, B.; Mullai, M.; Soundararajan, Soundarya; Selvakanmani, S.; Arun, D.

doi:10.1002/cae.22209

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…In another study, the authors developed a BCI for gathering data and detecting a learner’s mental state while watching MOOC (Massive Open Online Course) videos through EEG devices. Their proposal was based on John Sweller’s cognitive load theory to develop a model with preprocessed training data and test the classifiers to validate their ensemble classifiers’ performance [ 95 ]. Other studies have continued to explore the approach of assessing a learner’s engagement and attention during video lectures through inter-subject metrics [ 96 ].…”

Section: Resultsmentioning

confidence: 99%

“…MOOCs stand out among the most popular e-learning methods. In 2017, there were more than 58 million learners, 800 universities, and 9400 MOOCs on MOOC platforms and the leading MOOC, Coursera, has thirty million learners and 2700 different courses [ 95 ]. This shows the relevance of virtual education in the last decade, and with the COVID-19 pandemic, this e-learning tendency reached its peak [ 141 ].…”

Section: Discussionmentioning

confidence: 99%

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Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

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Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In another study, the authors developed a Brain-Computer Interface (BCI) for gathering data and detecting a learner's mental state while watching MOOCs (Massive Open Online Courses) videos through EEG devices. Their proposal was based on John Sweller's Cognitive Load Theory to develop a model with preprocessed training data and test the classifiers to validate their ensemble classifiers' performance [98]. Other studies have continued to explore the approach of assessing a learner's engagement and attention during video lectures through inter-subject metrics [99].…”

Section: Evolution Of Wbt In Educationmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Preprint

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Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting both the evolution of this field and its integration to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, the review sheds light on the potential implications of WBT for the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation with the EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

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“…Although there have been given EEG, EMG, EOG, EEG-EMG, EOG-EMG-based biosignal data capture applications developed for commercial or academic purposes in the literature, there isn't any study that allows the simultaneous evaluation of EEG, EMG, EOG and ECG biosignals together (Collins et al, 2016;Balamurugan et al, 2020;Teng et al, 2020). Researchers working in the field of biosignal processing and HCI may also prefer to work with data they have purchased from database banks or accessed from open-source databases, due to difficulties and costs of obtaining biosignals from different sources (Cavanagh et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

ÖNER¹,

Gürkan

KARAPINAR³

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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A multi-channel measurement system used to measure electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) biosignals has been designed and prototyped. The designed system has 16 configurable measurement channels. Of the 16 channels the developed system has, 8 have been designed for EEG, 2 for EMG, 2 for EOG, 1 for ECG measurements, the remaining 3 have been reserved as backup channels. In circuit design, biosignal amplifier design principles have been applied by taking into account the characteristics of the biosignal to be measured for each channel, such as bandwidth, frequency, amplitude, noise level. Modules such as instrumentation amplifier, filter, DC suppression unit, amplifier, DC level determination unit, analogdigital converter, optical isolation unit, power supply have been designed to perform biosignal measurements through these channels. Biosignals measured by the developed system can be shifted to the desired threshold level with the help of the analog output reference voltage, converted to digital data 10-bit resolution and transferred to the computer environment in real time. The data transferred to the computer can be used in C#, Excel, MATLAB, and LabVIEW platforms. The novelty of the developed system is that any of the four desired biosignal types can be measured from any channel. In addition, another feature of the system is that it can work with real-time data without being dependent on the databases serving for human-computer interface applications. In experimental studies with some researchers for the performance tests of the system, ECG, EEG, EMG and EOG signals have been recorded with different module configurations, and signal processing stages were carried out to be used for human-computer applications.

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Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Cited by 10 publications

References 32 publications

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology in Education: A Systematic Review

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

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