• International Journal of Knowledge Content Development & Technology
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• v.3 no.2
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• pp.29-42
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• 2013

The purpose of this study is to explore the information management behaviors of undergraduate students in their dormitory rooms, using Personal Information Management (PIM) as the theoretical framework. Ethnographic methods were applied to study how students devise their own systems combining digital and traditional tools to collect, create, manipulate, organize, and manage the information they need to fulfill their roles as university students. Results show a broad diversity of behaviors influenced more by individual learning styles and preferences than high-tech gadgetry. It is proposed that just as every individual has unique learning styles and preferences, so too do we have individual information styles, and we apply our tools and gadgets in our own ways to best accommodate our own styles.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.327-336
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• 2004

In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

• Journal of the Korean Home Economics Association
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• v.42 no.12 s.202
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• pp.219-230
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• 2004

The purpose of this study was to investigate relationships between the family strengths, mothers' self-efficacy, and their young children's teaming-related social skills and behavior problems. The subjects were the 217 children aged 4 to 6 years and their mothers at private child care centers in Seoul. The Family Strengths Scale and the Mothers' Self-Efficacy questionnaire were administered to the mothers. The teachers rated the learning-related social skills and problem behaviors of each child whose mother returned a set of Questionnaires. Analysis of variance revealed statistically significant differences in the family strengths according to the education level of the mothers. The mean the family strength score was higher for working mothers than for non-working mothers. The results of the test were statistically significant differences in the scores on the learning-related social skills and problem behaviors between boys and girls. The family strengths were positively correlated with the mothers' self-efficacy, and the learning-related social skills of the young children, while they were negatively correlated with problem behaviors.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.196-204
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• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• Journal of the Korean Home Economics Association
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• v.25 no.2
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• pp.39-54
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• 1987

One's Clothing Satisfaction, has a great influence on his role behavior in a society. Especially, as for the teacher's and the professors whose role is teaching, it can not be too emphaszed. Above all, high school girls and college women are the most likely to be influenced by their teachers' clothing behavior, to whom they pay their respect in every way. From this point of view, this study was aimed at explaining, the correlation between the teachers/professors' clothing behaviors and the learning efficiency. On clothing behaviors assessed 30 items related with the learning efficiency, selected from the instruments of preceding studies, and modified by the factors dealing with modesty, color, and design. The questionnaires were composed of two major categories: those for students and for teachers/progessors. The subjects of this study were included students and teachers/professors of high school and universities in Seoul. The data were analyzed statically by mean, standard Deviation, and F-test. The results of this study were summerized as follows; 1. The clothing behaviors of the teachers/professors influenced on the learning efficiency. Namely teachers' clothing satisfaction is in direct proportion to them teaching efficiency and students' learning efficiency. 2. Among the clothing behavior variables, modesty had a great influence on the learning efficiency, especially as for that of teachers more than professors. 3. The influence of the teachers'/professors' clothing behaviors on the learning efficiency showed no significance between high school girls and college women, but some significance between the grades of collegians. 4. As for the teachers'/professors' clothing behavior, male teachers/professors gave priority to design and female teachers/professors to modesty, color showed no significance between them irrespective of age. 5. As for the clothing behavior variabels, both the teachers/professors and the students showed some significance. In class the teachers/professors highly responded to their own clothing behaviors than the students. 6. According to priority the most favorable clothing colors for male teachers/professors are navy blue, gray, indigo blue, and black, and the most disgusting ones red, mud yellow, violet, pink, and green. The most favorable clothing colors for female teachers/professors are beign, white, pale yellow, and black, and the most disgusting ones red, mud yellow, and yellow and yellow according to priority. It is that teachers/professors should wear modestly and in color harmony to invite the desirable students' will to study. Teachers'/professors' colorful appearance and heavy toilet bring about a drop in the students' will to study.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.944-947
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• 1996

Realization of autonomous agents that organize their own internal structure in order to behave adequately with respect to their goals and the world is the ultimate goal of AI and Robotics. Reinforcement learning gas recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. In this paper, we present a method of reinforcement learning by which a multi robots learn to move to goal. The results of computer simulations are given.

• Korean Journal of Child Studies
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• v.26 no.4
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• pp.173-188
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• 2005

To examine relationships between learning-related social skills(LRSS) and literacy development a sample of 167 children aged 5-6 years were selected from 3 preschools in Seoul. Instruments for measuring LRSS were the cooperation, assertion, and self-control scales of the Social Skills Rating System (Gresham & Elliott, 1990) and the mastery behaviors scales of the Child Behavior Rating Scale (Bronson, et al. 1990). Literacy development was measured by the Concepts about Print(Woon, 1999), the Learning Readiness Scale(Korean Educational Development Institution 1998), and the Writing Development Scale(Lee, 1997). Teachers rated children's LRSS. Data were analyzed by mean, standard deviation, t-test, bivariate correlation, and regression analysis. LRSS correlated with children's literacy development. Mastery behaviors were the best predictor of literacy development.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.18 no.1
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• pp.58-65
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• 2007

Objectives : Motivational factor is a unique contributor to the typically poor academic performance of children with ADHD. However, few study has directly intervened learning motivation in children with ADHD. We conducted this study to explore the direct effects of the learning motivation improvement program applied to children with ADHD. Method The program was designed in order to increase an interest-inducing educational intervention, an academic skills integration, a basic learning activity (reading, writing, and math), and children's self-esteem. We conducted the program twice a week (total 10 sessions) and assessed learning motivation, teaming attitude, self-esteem, academic performance, and problem behaviors of participating children. Results : After the program, teachers reported improvement in teaming motivation. In addition, parents notified sisnificant reduction of problem behaviors. Children reported improvement in a few domains of teaming motivation and learning attitude. Conclusion : While loaming motivation is regarded as an important factor in education, there have been few studies considering this issue in both educational and psychiatric fields. The teaming motivation improvement would be needed in both field in order to reduce the deficits in academic performance in children with ADHD.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.77-90
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• 2020

The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.