Use of Personality Profile in Predicting Academic Emotion Based on Brainwaves Signals and Mouse Behavior

Azcarraga, Judith; Ibanez, John Francis I.; Lim, Ianne Robert; Lumanas, Nestor

doi:10.1109/kse.2011.45

Cited by 8 publications

(12 citation statements)

References 10 publications

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“…It should be emphasized that such "outlier" feature values that deviate significantly from the mean baseline figure may indicate that the EEG is picking up something unusual or the mouse is being handled or clicked somewhat differently (Azcarraga et al, 2011c). The findings not only show that there are significant increase in the prediction accuracy when the predictions are made once outliers are detected.…”

Section: Figure 2 a Feature Value Is Treated As Special And Is Consimentioning

confidence: 85%

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Recognizing Student Emotions using Brainwaves and Mouse Behavior Data

Azcarraga

Suarez

2013

International Journal of Distance Education Technologies

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Brainwaves (EEG signals) and mouse behavior information are shown to be useful in predicting academic emotions, such as confidence, excitement, frustration and interest. Twenty five college students were asked to use the Aplusix math learning software while their brainwaves signals and mouse behavior (number of clicks, duration of each click, distance traveled by the mouse) were automatically being captured. It is shown that by combining the extracted features from EEG signals with data representing mouse click behavior, the accuracy in predicting academic emotions substantially increases compared to using only features extracted from EEG signals or just mouse behavior alone. Furthermore, experiments were conducted to assess the prediction accuracy of the system at points during the learning session where several of the extracted features significantly deviate in value from their mean. The experiments confirm that the prediction performance increases as the number of feature values that deviate significantly from the mean increases.

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Section: Figure 2 a Feature Value Is Treated As Special And Is Consimentioning

confidence: 85%

“…Azcarraga et al (2011c) considers some feature values to be "special", signifying that something caused to be distinct from the other features values. These special feature values are referred to as an "outlier" if they deviate by at least one standard deviation from the mean.…”

Section: Selective Predictionmentioning

confidence: 99%

Recognizing Student Emotions using Brainwaves and Mouse Behavior Data

Azcarraga

Suarez

2013

International Journal of Distance Education Technologies

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“…The study of Iventado et al [40] applied the EEG to extract the learners' emotions to decide the proper time for learning mediate, also to measure feedback. The work of Azcarraga et al [41] showed the EEG had low accuracy representing brainwave data, however adding the mouse improved the accuracy to 92%. The work of ABE conducted by Gonzales-Sanchez et al in [42] is a compounded system with Emotive Epoc, MIT mind reader, pressure sensors and skin conductance to track learner emotions, to provide a system that reduces the negative emotions.…”

Section: B Other Monitoring Toolsmentioning

confidence: 99%

An Experimental Study of Learning Behaviour in an ELearning Environment

Alhasan

Chen

2018

2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference On

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To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.

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“…Researchers work to find a solution for the lack of emotional communication in distance education, taking into account that emotions are also effective in learning [2,4,26,27,29,34,37]. However, it is seen that these studies are mostly related to emotional information acquisition and parametric modeling.…”

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confidence: 99%

The effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements

Özek

2018

Comp Applic In Engineering

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Technology diverts engineering education into learning management system for distance education activities. Learning management system facilitates the management of education and training process, including instructor‐student, student‐student, and student‐content communication. Nevertheless, distance education has some disadvantages of not being able to establish active communication with students as in traditional education. This research offers a system which merges student emotion recognition with learning management system. This study aims to investigate the effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements. In this research, the quasi‐experimental design adopted 103 engineering students who were divided into three groups: the control group (participates in face‐to‐face education), the experimental group I (uses a system that merges emotion recognition with learning management system), and the experimental group II (uses learning management system). As a result of the research, it was found that the academic achievement and motivation of the experimental group I was significantly better than performance of the experimental II and the control group.

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Use of Personality Profile in Predicting Academic Emotion Based on Brainwaves Signals and Mouse Behavior

Cited by 8 publications

References 10 publications

Recognizing Student Emotions using Brainwaves and Mouse Behavior Data

Recognizing Student Emotions using Brainwaves and Mouse Behavior Data

An Experimental Study of Learning Behaviour in an ELearning Environment

The effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements

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