• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.585-595
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• 2017

This study investigates how the emotion-based learning motivation enhancement program influences learning motivation and social support of college students. The developed final program consists of Learning Motivation I, Learning Coaching, and Learning Motivation II, which has 12 sessions. In each session, every student was guided to have reflection time by writing self-evaluation and reflection paper. The participants were 38 students majoring in engineering at K-college located in G city who took one liberal arts subject based on psychology during the 1st semester in 2016 and who were divided into an experimental group (19 students) and a control group (19 students) by non-probability sampling method. In the experimental group, emotion-based learning motivation enhancement program was totally processed 12 times, one class in a week, by one main lecturer and one assistant lecturer. For data analysis, independent sample t-tests, paired samples t-tests, and review analysis were conducted. The study results are as follows. First, the experimental group participating in emotion-based learning motivation enhancement program had more significant differences in learning motivation, and both self-confidence and self-contentment among sub-components than the control group. Second, the experimental group had no significant differences in social support, compared with the control group. The impression writing analysis of the experimental group showed that this program affected learning motivation and social support. Lastly, the study discussions and implications are described.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.225-234
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• 2010

To investigate the role of positive emotion in education, we have reviewed the previous studies on positive emotion, learning and motivation. In the present study, we examined the definition of positive emotion, and influences of positive emotion on cognition, creativity, social relationship, psychological resource such as life satisfaction, and interactive relationship among positive emotion, motivation and learning. To investigate the role of positive emotion on motivation and learning more scientifically, we examined the recent results of neuroscience. In other words, we have reviewed diverse research on positive emotion, learning and motivation based on brain-based learning. We also examined the research of autonomy-supportive environment as the specific example of improving positive emotion. As one of the most effective methods for emotional education, we discussed brain-based learning, the new research field. As the future prospects, we discussed the implications, possibilities and limitations of brain-based learning.

• Journal of the Korean Home Economics Association
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• v.49 no.3
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• pp.11-21
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• 2011

The purposes of this study were to explore the gender differences in children's behavior problems, emotional regulation and social support, and to investigate differences between boys and girls in the interrelationships between these kinds of variables. The participants were 189 children in 4 to 6 grades and their teachers from one elementary school in Seoul. The data were analyzed using descriptive statistics, t-test, Pearson's correlation, and multiple regression. The results were as follows: (1) There were statistically significant gender differences in the children's behavior problems, emotional regulation and social support. (2) Children's negative emotion explained boys and girls acting out problems and learning problems. Children's positive emotion regulation explained boys' and girls' shy-anxious and learning problems. Boys, who perceived less support from parents, displayed more acting out behavior, boys who perceived less supports from friends showed more shy-anxious behavior, and boys who perceived less supports from teachers exhibited more learning problems.

• Educational Technology International
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• v.23 no.1
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• pp.1-39
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• 2022

We study emotion regulation in a distant CABLe (Collaborative Argumentation Based-Learning) setting at university. We analyze how students achieve the group task of synthesizing the literature on a topic through scientific argumentation on the institutional Moodle's forum. Distinguishing anticipatory from reactive emotional regulation shows how essential it is to establish and maintain a constructive working climate in order to make the best out of disagreement both on social and cognitive planes. We operationalize the analysis of anticipatory emotional regulation through an analytical grid applied to the data of two groups of students facing similar disagreement. Thanks to sharp anticipatory regulation, group 1 solved the conflict both on the social and the cognitive plane, while group 2 had to call out for external regulation by the teacher, stuck in a cyclically resurfacing dispute. While the institutional digital environment did afford anticipatory emotional regulation, reactive emotional regulation rather occurred through complementary informal and synchronous communication tools. Based on these qualitative case studies, we draw recommendations for fostering distant CABLe at university.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.91-101
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• 2008

For a friendly interaction between human and robot, emotional interchange has recently been more important. So many researchers who are investigating the emotion generation model tried to naturalize the robot's emotional state and to improve the usability of the model for the designer of the robot. And also the various emotion generation of the robot is needed to increase the believability of the robot. So in this paper we used the hybrid emotion generation architecture, and defined the generalized context input of emotion generation model for the designer to easily implement it to the robot. And we developed the personality and loyalty model based on the psychology for various emotion generation. Robot's personality is implemented with the emotional stability from Big-Five, and loyalty is made of familiarity generation, expression, and learning procedure which are based on the human-human social relationship such as balance theory and social exchange theory. We verify this emotion generation model by implementing it to the 'user calling and scheduling' scenario.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.490-499
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• 2014

This research was conducted to verify the moderating effect of gender, which impacts learning strategy, negative emotions, and influence that university life adjustment of undergraduates has on academic achievement. Therefore, this survey was conducted on learning strategy and negative emotion in February, targeting 654 freshmen of a university in Seoul on their academic and social adaptation and grades which has been measured and analyzed three months later at the end of the term. The moderating effect according to genders was analyzed through hierarchical regression analyses, and diagram was presented after conducting the simple gradient verification as a post analysis on interactive effect. As a result of analysis, although learning strategy and academic adaptation was appeared to be significantly affecting grades regardless of gender, the impact of negative emotions on academic achievement were significant only to females, and the impact of social adaptation on academic achievement was significant only to males, which enabled the researchers to confirm the regulation effects on different genders. The implications and proposal for a follow-up study about learning strategy, emotion, and adaptation based on the research resulted in the discussion of academic achievement in university.

• 대한공업교육학회지
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• v.42 no.2
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• pp.47-66
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• 2017

As the necessity of humanistic education has recently been socially emphasized, aptitude and character test has been conducted in the stage of training instructors to focus on making them equipped with proper ability of humanistic education. Especially, considering characteristics of students in specialized high schools with stronger negative concept of ego along with higher proportion of students looking for job in early ages compared to regular high schools, instructors in specialized high schools need to pay more attention on humanistic education of students compared to those in regular high schools. Therefore, this study has developed social emotion learning (SEL) of preliminary teachers in specialized high schools and analyzed the influence of developed programs on characters in teaching positions of preliminary instructors in specialized high schools. In this study, ADDIE model has been used developing SEL programs, and developed programs were comprised of total six sessions. Social emotion learning has been performed on 27 preliminary teachers in experiment group, and regular academic education courses were given on 30 preliminary teachers in control group. At this time, as for class contents, 'self-recognition' has been dealt with in the first and second sessions followed by 'self-management' in the second and third sessions, 'adjustment in relationship' in the fourth session, 'responsible decision making' in the fifth session, and 'personal relationship' in the sixth session. Classes have been conducted for 90 minutes in average. Since intentional sampling method has been used in this study, difference of pre-scores between groups might influence on the difference on post-scores. Therefore, ANCOVA that adjusted the pre-scores to be consistent has been utilized. As a result, there was a significant difference on the post-scores in characters in teaching positions between experiment group and control group.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.