The performance of university students during their academic session are vital to their overall grade throughout their term in the university. There are multiple factors that could lead to the loss of performance but the foremost factor is their level of emotions. Previous research has shown that to determine the performance of the students, the best way to do so is by analysing their attention levels. With the development of portable Electroencephalogram (EEG) devices and machine learning algorithms, it is easy to obtain the students attention and emotion level during their academic sessions. This paper aims to present a method of obtaining the EEG signals using a portable EEG device and classifying it into the type of emotions that are present in the human brain. The EEG device will obtain the attention level and EEG signals during two scenarios which are lectures/tutorials and exams/quizzes. The signals are then compiled and analysed to determine the emotion labels based on a normalization process that categories the signals into positive or negative emotions. The dataset and labels are then used to train and evaluate multiple machine learning models and a deep learning model in order to determine which model has the best accuracy and performance. The chosen model is then used to predict the emotions of several students during both scenarios and the average emotions are then compared with their average attention to determine the effect of emotions on the students’ performance. Hence, this paper will first provide a method on obtaining the emotion labels, followed by the models’ development and finally correlating the predicted emotions with the students’ performance during their academic sessions.
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22 November 2022
THE 3RD INTERNATIONAL CONFERENCE OF INFORMATION & COMMUNICATION TECHNOLOGY 2021 (ICICTM 2021)
23 March 2021
Kuala Lumpur, Malaysia
Research Article|
November 22 2022
A machine learning approach using EEG signals to identify emotions and performance level among university students
Mohd Fahmi Mohamad Amran;
Mohd Fahmi Mohamad Amran
a)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
a)Corresponding author: [email protected]
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Venothanee Sundra Mohan;
Venothanee Sundra Mohan
b)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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Nurhafizah Moziyana Mohd. Yusop;
Nurhafizah Moziyana Mohd. Yusop
c)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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Yuhanim Hani Yahaya;
Yuhanim Hani Yahaya
d)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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Tengku Mohd Tengku Sembok;
Tengku Mohd Tengku Sembok
e)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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Siti Rohaidah Ahmad;
Siti Rohaidah Ahmad
f)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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Mohd Afizi Mohd Shukran
Mohd Afizi Mohd Shukran
g)
Computer Science Department, Faculty of Science and Defense Technology, Universiti Pertahanan Nasional Malaysia
, Kuala Lumpur, Malaysia
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AIP Conf. Proc. 2617, 020011 (2022)
Citation
Mohd Fahmi Mohamad Amran, Venothanee Sundra Mohan, Nurhafizah Moziyana Mohd. Yusop, Yuhanim Hani Yahaya, Tengku Mohd Tengku Sembok, Siti Rohaidah Ahmad, Mohd Afizi Mohd Shukran; A machine learning approach using EEG signals to identify emotions and performance level among university students. AIP Conf. Proc. 22 November 2022; 2617 (1): 020011. https://doi.org/10.1063/5.0120547
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