TY - GEN
T1 - Automatic Cognitive Workload Classification Using Biosignals for Distance Learning Applications
AU - Varandas, Rui
AU - Gamboa, Hugo
AU - Silveira, Inês
AU - Gamboa, Patrícia
AU - Quaresma, Cláudia
N1 - info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F150304%2F2019/PT#
info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F150672%2F2020/PT#
PY - 2021
Y1 - 2021
N2 - Current e-learning platforms provide recommendations by applying Artificial Intelligence algorithms to model users’ preferences based on content, by collaborative filtering, or both, thus, do not consider users’ states, such as boredom. Biosignals and Human-Computer Interaction will be used in this study to objectively assess the state of the user during a learning task. Preliminary data was obtained from a small sample of young adults using physiological sensors (e.g., electroencephalogram, EEG, and functional near infrared spectroscopy, fNIRS) and computer interfaces (e.g., mouse and keyboard) during cognitive tasks and a Python tutorial. Using Machine Learning (ML), Cognitive Workload was classified considering EEG and fNIRS. The results show that it is possible to automatically distinguish cognitive states with accuracy around 84%. This procedure will be applied to adjust the difficulty level of learning tasks, model user preferences, and ultimately optimize the distance learning process in real-time, in a future e-learning platform.
AB - Current e-learning platforms provide recommendations by applying Artificial Intelligence algorithms to model users’ preferences based on content, by collaborative filtering, or both, thus, do not consider users’ states, such as boredom. Biosignals and Human-Computer Interaction will be used in this study to objectively assess the state of the user during a learning task. Preliminary data was obtained from a small sample of young adults using physiological sensors (e.g., electroencephalogram, EEG, and functional near infrared spectroscopy, fNIRS) and computer interfaces (e.g., mouse and keyboard) during cognitive tasks and a Python tutorial. Using Machine Learning (ML), Cognitive Workload was classified considering EEG and fNIRS. The results show that it is possible to automatically distinguish cognitive states with accuracy around 84%. This procedure will be applied to adjust the difficulty level of learning tasks, model user preferences, and ultimately optimize the distance learning process in real-time, in a future e-learning platform.
KW - Artificial intelligence
KW - Biosignals
KW - Distance-learning
KW - Human-computer interaction
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85112001239&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78288-7_24
DO - 10.1007/978-3-030-78288-7_24
M3 - Conference contribution
AN - SCOPUS:85112001239
SN - 978-3-030-78287-0
T3 - IFIP Advances in Information and Communication Technology
SP - 254
EP - 261
BT - Technological Innovation for Applied AI Systems - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferreira, Pedro
A2 - Brito, Guilherme
PB - Springer
CY - Cham
T2 - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
Y2 - 7 July 2021 through 9 July 2021
ER -