Detecting Change in Engineering Interest in Children through Machine Learning using Biometric Signals

Olivas, Gustavo; Candela, Milton; Ocampo, J. A.; Acosta, L.; Aguilar, Aime; Delgado, Esther; Alanis, Myriam; Ramírez, Mauricio Pineda; Lozoya, Jorge de J.; Murrieta, Jorge; Ramírez, Ricardo

doi:10.1109/ieeeconf53024.2021.9733772

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Regarding attention, considering that it is the most important factor in learning, protocols have been proposed to classify the levels of attention in educational environments [ 117 ]. Other studies have generated offline algorithms to evaluate primary and middle-school children’s STEM interests [ 118 ]. Wearable technologies can also make EEG research more approachable and accessible.…”

Section: Resultsmentioning

confidence: 99%

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%

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 tree-based models such as Extra-Trees, a feature's importance is determined by the frequency and depth of its use for data splits across all trees (Olivas et al, 2021 ). A feature frequently used and closer to the tree roots is considered more crucial.…”

Section: Methodsmentioning

confidence: 99%

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Blanco-Ríos,

Candela-Leal,

Orozco-Romo

et al. 2024

Front. Hum. Neurosci.

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Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.

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“…Regarding attention, considering that it is the most important factor in learning, protocols have been proposed to classify the levels of attention in educational environments [122]. Other studies have generated offline algorithms to evaluate primary and middle school children's STEM interests [123]. Wearable technologies can also make EEG research more approachable and accessible.…”

Section: Applications Of Wbts In Educationmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Detecting Change in Engineering Interest in Children through Machine Learning using Biometric Signals

Cited by 3 publications

References 26 publications

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology in Education: A Systematic Review

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Wearable Biosensor Technology in Education: A Systematic Review

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