• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.231-236
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• 2018

There has been a notable increased interest of the study of emotions in educational contexts. The purpose of this study was to analyze predicting emotional variables of self-regulated learning strategies and self-handicapping strategies with the university students. Participants were 143 students of undergraduates at A University and B University. Collected data were analyzed by correlation analysis and regression analysis, respectively. It turned out that class related emotions, learning related emotions, and test emotions predicted self-handicapping strategies negatively. However, achievement emotions didn't predict self-regulated learning strategies. The result of this study will provide the theoretical basis and practical usefulness of academic emotions.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.53-64
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• 2014

It is important to keep learners' feeling positive during learning to enhance learning performance. According to flow theory,challenge-skill balance is a precondition for flow experience: Learners feel anxiety when the challenge of learning is higher than their ability, feel boredom when the challenge of learning is lower than learners' ability, and engage in flow status when the challenge of learning matches the learners' ability. However, the current empirical study reveals that emotions related to enjoyment may appear when the learners' skill is equal to or higher than the learning challenge. Nevertheless, boredom emotion may appear when learners perceive the courses are difficult but unimportant. These empirical survey results revealed the necessary of rethinking the appearance of boredom and enjoyment emotions in computer education.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.103-114
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• 2021

The purpose of this study is to investigate the history of and recent trends in science education research on emotion and explore the direction of future development. A comprehensive review of literature was conducted, and the results were organized according to research questions. Science education research on emotion began in the state of confusion because a number of concepts coexisted and overlapped in the concept of affect. More systematic approaches were then used when science-related attitudes were divided into the two categories of scientific attitudes and attitudes toward science. The research continued to study on positive and negative emotions relevant to science learning. However, the complex relationship between cognition and emotion and the limitation of the dichotomy dealing with emotions as external factors influencing student learning were revealed. By contrast, the recent research on epistemic emotions were based on the new perspective that scientific practices are accompanied with emotions and that cognition and emotion are integrated into the practices, influencing each other. Therefore, research should be carried out in ways that can help science educators understand a variety of emotions emerging in learning science through scientific practices and respond appropriately to even negative emotions of students.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.467-476
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• 2020

The purpose of this study is as a base study for improving the quality of online classes through multilateral analysis that examines the learning outcoms of educational quality and learning emotions on non-face-to-face online classes at Technical Colleges To this study, from March 1, 2020 to August 31, 2020, a survey was conducted on 1,000 students of H Technical Colleges located in the metropolitan area. The collected data were statistically processed using the SPSS Statistics 18.0 program, t-validation were performed to reveal awareness of online class also correlation analysis and multiple regression analysis were performed to reveal the relation and influence of factors related to quality of instruction, learning emotions, learning outcomes. First, there was a statistically significant difference in perception of online classes by gender and grade. Second, there was a positive correlation between the educational quality, learning emotions, and learning outcomes for online classes. Third, among the learning outcomes, the factors that influence the achievement were the educational content and positive emotions, and the factors that influence the satisfaction among the learning outcomes were the educational content and the learning environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1759-1768
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• 2022

This paper proposes a model to detect depression-related emotions in a user's speech using wellness dialogue scripts provided by AI Hub, topic-specific daily conversation datasets, and chatbot datasets published on Github. There are 18 emotions, including depression and lethargy, in depression-related emotions, and emotion classification tasks are performed using KoBERT and KOELECTRA models that show high performance in language models. For model-specific performance comparisons, we build diverse datasets and compare classification results while adjusting batch sizes and learning rates for models that perform well. Furthermore, a person performs a multi-classification task by selecting all labels whose output values are higher than a specific threshold as the correct answer, in order to reflect feeling multiple emotions at the same time. The model with the best performance derived through this process is called the Depression model, and the model is then used to classify depression-related emotions for user utterances.

• Journal of The Korean Association For Science Education
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• v.34 no.8
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• pp.745-754
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• 2014

Students experience a variety of achievement-related emotions during the process of learning the science curriculum. The purpose of this study is to develop an achievement emotions questionnaire for Korean middle school science curriculum to measure the achievement emotions that middle school students experience during study of this curriculum, and verified its validity. The Achievement Emotions Questionnaire-Korean Middle School Science is based on the English version of the Achievement Emotions Questionnaire, developed with reference to Korean middle school science curriculum and the characteristics of science study, from the perspective of the control-value theory of achievement. It has 232 questions, configured to measure nine achievement emotions across three types of academic settings. The questionnaire results can be treated with a high degree of confidence according to the result of our validation, which also verified that the achievement emotions of these students are configured with four internal criteria (learning strategy, achievement motivation and course grade), as suggested by the control-value theory; this in turn verifies that the nine achievement emotions are sufficiently distinctive across study situations. Last, it was verified that the questionnaire has sufficient external validity based on a comprehensive examination of the relation between science achievement emotions and the four criterion variables for each student. This suggests that through the development and implementation of this quantitative questionnaire, basic ground was provided to understand the achievement emotions experienced by middle school students learning the science curriculum.

• ETRI Journal
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• v.35 no.5
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• pp.869-879
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• 2013

This study is related to the acquisition of physiological signals of human emotions and the recognition of human emotions using such physiological signals. To acquire physiological signals, seven emotions are evoked through stimuli. Regarding the induced emotions, the results of skin temperature, photoplethysmography, electrodermal activity, and an electrocardiogram are recorded and analyzed as physiological signals. The suitability and effectiveness of the stimuli are evaluated by the subjects themselves. To address the problem of the emotions not being recognized, we introduce a methodology for a recognizer using prototype-based learning and particle swarm optimization (PSO). The design involves two main phases: i) PSO selects the P% of the patterns to be treated as prototypes of the seven emotions; ii) PSO is instrumental in the formation of the core set of features. The experiments show that a suitable selection of prototypes and a substantial reduction of the feature space can be accomplished, and the recognizer formed in this manner is characterized by high recognition accuracy for the seven emotions using physiological signals.

• Journal of Korean society of Dental Hygiene
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• v.21 no.6
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• pp.805-812
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• 2021

Objectives: To investigate the perceived quality of classes, academic emotions, and learning achievement levels associated with the non-face-to-face classes of health science students, and to analyze the factors related to class satisfaction. Methods: Using a questionnaire, 238 health science students were surveyed regarding the quality of classes, academic emotions, and learning achievement levels. Factors related to calss satisfaction were analyzed using stepwise multiple regression. Results: Lecture types that the students were most satisfied with were 'video lectures using PPT' and 'recorded lectures provided by LMS', while 'real-time video lectures' were scored the lowest (p=0.005). Factors affecting non-face-to-face class satisfaction were perceived achievement (β=0.425, p<0.001), learning content (β=0.265, p<0.001), learning emotion (β=0.171, p<0.001), and learning environment (β=0.137, p=0.012). The adjusted explanatory power for this model was 63.9%. Conclusions: To increase the satisfaction of health science students with non-face-to-face classes, it is necessary to prepare an institutional foundation and to develop an educational program that can increase perceived achievement.