TY  - JOUR
Y1  - 2022///
SN  - 20763417
PB  - MDPI
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122092200&doi=10.3390%2fapp12010389&partnerID=40&md5=4675b933ae7cf13b49d306c0ebf06128
JF  - Applied Sciences (Switzerland)
A1  - Othman, E.S.
A1  - Faye, I.
A1  - Hussaan, A.M.
VL  - 12
AV  - none
N1  - cited By 0
N2  - The usage of physiological measures in detecting studentâ??s interest is often said to improve the weakness of psychological measures by decreasing the susceptibility of subjective bias. The existing methods, especially EEGâ??based, use classification, which needs a predefined class and complex computational to analyze. However, the predefined classes are mostly based on subjective measurement (e.g., questionnaires). This work proposed a new scheme to automatically cluster the students by the level of situational interest (SI) during learningâ??based lessons on their electroencephalography (EEG) features. The formed clusters are then used as ground truth for classification purposes. A simultaneous recording of EEG was performed on 30 students while attending a lecture in a real classroom. The frontal mean delta and alpha power as well as the frontal alpha asymmetry metric served as the input for kâ??means and Densityâ??Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithms. Using the collected data, 29 models were trained within nine domain classifiers, then the classifiers with the highest performance were selected. We validated all the models through 10â??fold crossâ??validation. The high SI group was clustered to students having lower frontal mean delta and alpha power together with negative Frontal Alpha Asymmetry (FAA). It was found that kâ??means performed better by giving the maximum performance assessment parameters of 100 in clustering the students into three groups: high SI, medium SI and low SI. The findings show that the DBSCAN had reduced the performance to cluster dataset without the outlier. The findings of this study give a promising option to cluster the students by their SI level, as well as address the drawbacks of the existing methods, which use subjective measures. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
IS  - 1
ID  - scholars17798
TI  - Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features
ER  -