• Educational Technology International
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• v.18 no.2
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• pp.101-129
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• 2017

This study attempted to collect learners' emotion and state motivation, analyze their inner states, and measure state motivation using a non-self-reported survey. Emotions were measured by learning segment in detailed learning situations, and they were used to indicate total state motivation with prediction power. Emotion was also used to explain state motivation by learning segment. The purpose of this study was to overcome the limitations of video-based learning environments by verifying whether the emotions measured during individual learning segments can be used to indicate the learner's state motivation. Sixty-eight students participated in a 90-minute to measure their emotions and state motivation, and emotions showed a statistically significant relationship between total state motivation and motivation by learning segment. Although this result is not clear because this was an exploratory study, it is meaningful that this study showed the possibility that emotions during different learning segments can indicate state motivation.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.188-194
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• 2019

Always, emotion is mutant. This is principle of literary treatment. In the literature, sadness is not sadness, and 'loving emotion' is not 'loving emotion.' Despite loving of our, loving is sadness. Also loving is to cry. This crying becomes love. This study is to show the mutant emotion which is to be able to code Deep Learning AI. We explored the Sijo "Streams that cried last night", because this Sijo was useful to study mutant emotion. The result was that this Sijo was coding the mutant emotion. Almost continuously, the sadness codes were spawning and concentrating. So this Sijo was making the emotion of love with the sadness. If this study is continued, It is believed that our lives will be much happier. And the method of literary therapy will be able to more upgrade.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6940-6948
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• 2015

This study is a professor of media literacy and self-expression and emotion regulation strategies between was conducted to analyze the impact on the self-regulated learning abilities of youth, Media literacy has showed significant influence on self-regulated learning abilities of young people, self-expression and emotion regulation strategies had significant influence on self-regulated learning abilities of young people. In addition, media literacy, self-expression, emotion regulation strategy was found to significantly affect the path to self-regulated learning abilities of young people. Therefore, media literacy plays an important role in the self-regulated learning abilities of young people, self-expression and emotion regulation strategies was found that the effect is mediated between media literacy and self-regulated learning abilities of young people. The results of this study means a lot of hard work and training programs are needed for improving self-regulated learning and self-expression and emotion regulation strategies of youth through the school curriculum and education on media literacy era, which we hope in the future the youth of life necessary for self-expression, emotion regulation strategies, suggest to improve as a practical implication offers a number of implications for school education.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.32-33
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• 2012

• Communications of Mathematical Education
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• v.33 no.3
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• pp.181-205
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• 2019

This study analyzed how university students' motivations for learning mathematics and emotions for learning mathematics occur and how they relate to each other by introducing the factor called needs in the particular context of mathematics learning, mathematics class centered on peer discussions. We conceptualized the key concepts of the study, motivation for learning mathematics and emotion for learning mathematics. Based on them, we drew specific ways to observe motivation and emotion for learning mathematics and conduct the research. As a result, motivations for learning mathematics occurred to satisfy some needs. Also, positive emotions for learning mathematics occurred when some needs were satisfied, whereas negative emotion for learning mathematics occurred when some needs were not satisfied. Furthermore, when the needs leading to motivations for leaning mathematics were satisfied, positive emotions for learning mathematics occurred. The unfulfilled needs leading to negative emotions for learning mathematics make motivations for learning mathematics occur to satisfy those needs.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.351-360
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• 2018

Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.83-89
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• 2021

This study examined how the learning styles and emotions of 234 students enrolled in early childhood education-related departments affected their ability to participate in the learning community. The research collected the variables of the pre-service early childhood teachers' ability to participate in the learning community, the emotions that they experienced while learning, and their learning styles. The collected data were analyzed through correlation analysis and regression analysis. The results indicate that the students' ability to participate in the learning community was positively correlated with the following: their analytical learning attitude (among learning styles); learning motivation; degree of preference for discussion and discussion types; positive and negative emotions, which are subcategories of learning emotion; and the degree of preference for experimentation and their practice type among teaching methods. Second, regression analysis showed that the students' ability to participate in the learning community could be predicted by negative emotion as a subcategory of their learning emotion, learning motivation, degree of preference for the experiment and practice type, their analytical learning attitude, and the degree to which students value studying the content of their major subjects.