• Journal of Wellbeing Management and Applied Psychology
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• v.7 no.1
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• pp.43-50
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• 2024

Purpose: People must express their feelings and thoughts to maintain mental health and stability. Expressing one's emotions, experiences, and thoughts in writing relieves inner feelings, promotes self-exploration, and improves individual well-being, resulting in a pleasant state on physical, mental, and social levels. This study aims to reveal the learning experiences of university students who participated in a self-expressive writing course to improve their well-being. Method: To explore the learning experiences of university students who took a self-expressive writing course, this study used qualitative research methods to analyze the students' written reflection notes. Results: Self-expressive writing was found to resolve university students' negative emotions, regulate their emotions, improve their self-reflection and self-awareness, contributing to their problem-solving skills and ability to set new goals, and strengthen their social communication. The meaning of this class experience can be summarized as healing, awareness, reflection, change, and growth. Conclusion: The results of this study provide concrete data on expressive writing classes and are valuable when designing the writing programs.

• 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.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.367-372
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• 2021

The purpose of this study is to suggest that the movement expression activity of intellectually disabled people is effective in the learning process of LMA motion recognition based on Kinect sensor. We performed an ICT motion recognition games for intellectually disabled based on movement learning of LMA. The characteristics of the movement through Laban's LMA include the change of time in which movement occurs through the human body that recognizes space and the tension or relaxation of emotion expression. The design and implementation of the motion recognition model will be described, and the possibility of using the proposed motion recognition model is verified through a simple experiment. As a result of the experiment, 24 movement expression activities conducted through 10 learning sessions of 5 participants showed a concordance rate of 53.4% or more of the total average. Learning motion games that appear in response to changes in motion had a good effect on positive learning emotions. As a result of study, learning motion games that appear in response to changes in motion had a good effect on positive learning emotions

• 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.

• International Journal of Contents
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• v.15 no.2
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• pp.59-74
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• 2019

The objective of this study was to analyze the influence of physical education classes based on Flipped Learning on self-directed learning abilities and learning attitude towards these classes, for middle school students. The study selected 90 students as an experimental group (3 classes) and 97 students as a control group (3 classes), among 240 students of the first-year students attending a middle school located at Jeonju City of South Korea, applying convenience sampling, one of the non-probability sampling methods. For the experimental group, 36 sessions of physical education classes were held for 14 weeks, while the control group received teacher-centered classes. Comparing the results with the control group, the experimental group showed significant differences in terms of all sub factors of self-directed learning abilities, namely; desire for learning, learning objective establishment, basic self-management abilities, selection of learning strategy and self-reflection. Moreover, the experimental group manifested significant differences in terms of all sub factors of attitude towards the physical education subjects, namely; positive emotions, negative emotions, health & physical strength, interpersonal relations, physical activities & movements, and active participation & positive performance. From the findings, it can be considered that physical education classes based on Flipped Learning contributed to improving self-directed learning abilities and attitude towards physical education classes. This result can serve as a significant basic material for designing and performing classes in raising the understanding of Flipped Learning and effectively applying Flipped Learning in physical education classes.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.232-239
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• 2014

As online texts have been rapidly growing, their automatic classification gains more interest with machine learning methods. Nevertheless, comparatively few research could be found, aiming for Korean texts. Evaluating them with statistical methods are also rare. This study took a sample of tweets and used machine learning methods to classify emotions with features of morphemes and n-grams. As a result, about 76% of emotions contained in tweets was correctly classified. Of the two methods compared in this study, Support Vector Machines were found more accurate than Na$\ddot{i}$ve Bayes. The linear model of SVM was not inferior to the non-linear one. Morphological features did not contribute to accuracy more than did the n-grams.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.359-376
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• 2021

In this study, in accordance with the research trend that the learner's emotions expressed positively or negatively in mathematics learning or the learner's beliefs and attitudes toward mathematics learning affect the results of mathematics learning, the learner's emotions and affective factors are analyzed in the learner's own learning. A power that can be adjusted according to a goal or purpose is needed, and I tried to explain this power through meta-affect. To this end, the meaning of the definitional and conceptual factors of meta-affect was explored based on prior studies. Affective factors of meta-affect were viewed as emotions, attitudes, and beliefs, and conceptual factors of meta-affect were viewed as awareness, evaluating, controlling, utilization, and monitoring, and the meaning of each conceptual factor was also defined. In this study, the conceptual factors and meanings of meta-affect in terms of using them to help in learning mathematics by controlling them, beyond the identification or examination of the characteristics of the affective factors, which are meaningfully dealt with in the field of mathematics education.