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. 2022 Apr 7;12(1):5857.
doi: 10.1038/s41598-022-09578-y.

EEG-based measurement system for monitoring student engagement in learning 4.0

Andrea Apicella  1 , Pasquale Arpaia  2 , Mirco Frosolone  1   3 , Giovanni Improta  3 , Nicola Moccaldi  1 , Andrea Pollastro  1
Affiliations

EEG-based measurement system for monitoring student engagement in learning 4.0

Andrea Apicella et al. Sci Rep. .

Abstract

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The architecture of the system for engagement assessment; the white box is active only in the cross-subject case (ADC-Analog Digital Converter, CSP-Common Spatial Pattern, TCA-transfer component analysis, and SVM-support vector machine).
Figure 2
Figure 2
(a) EEG-signal acquisition device Helmate8 from abmedica, and (b) examples of its dry electrodes.
Figure 3
Figure 3
Screenshots from the CPT game. At the beginning of the game (a), the cross starts to run away from the center of the black circumference. The user goal is to bring the cross back to the center by using the mouse. At the end of each trial (b), the score indicates the percentage of time spent by the cross inside the circumference.
Figure 4
Figure 4
Within-subject performances of the compared processing techniques SVM, k-NN, ANN, LDA, DNN and CNN in (a) cognitive engagement and (b) emotional engagement detection. Each bar describes the average accuracy over all the subjects.
Figure 5
Figure 5
Filter Bank impact on the class (red and blue points) separability. t-SNE-based features plot of five subjects randomly sampled (first row: without Filter Bank; second row: with Filter Bank).
Figure 6
Figure 6
A comparison using t-SNE of the FBCSP data first (a) and after (b) removing the average value of each subject, in the cross-subject approach. The colors (red and blue) correspond to the two classes, the numbers identify the individuals.
Figure 7
Figure 7
The Self Assessment Manikin. Scale for Valence Assessment.
See this image and copyright information in PMC

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