• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.239-259
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• 2009

The study aims at investigating students' behaviors and perceptions regarding the collaborative learning based group project using the wiki environment. The study utilized Google Sites as a case, and analyzed file unloads, the use of web pages, navigation bars, and comments as well as surveys. The study discusses main characteristics of students' activities in the collaborative learning group project, which are drawn from the analysis of students' behaviors and perceptions. The study also provides implications for improvement of wiki environment to support collaborative learning in education.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.259-265
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• 2006

Learning and emotions are very important parts to implement intelligent robots. In this paper, we implement intelligent virtual character based on reinforcement learning which interacts with user and have internal emotion model. Virtual character acts autonomously in 3D virtual environment by internal state. And user can learn virtual character specific behaviors by repeated directions. Mouse gesture is used to perceive such directions based on artificial neural network. Emotion-Mood-Personality model is proposed to express emotions. And we examine the change of emotion and learning behaviors when virtual character interact with user.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• IE interfaces
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• v.16 no.1
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• pp.54-62
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• 2003

With the ubiquitous use of the Internet in daily business activities, most of modern firms are keenly interested in customer's behaviors on the Internet. That is because a wide variety of information about customer's intention about the target web site can be revealed from IP address, reference address, cookie files, duration time, all of which are expressing customer's behaviors on the Internet. In this sense, this paper aims to accomplish an objective of analyzing a set of exemplar web log files extracted from a specific P2P site, anti identifying information about customer segmentation strategies. Major web mining technique we adopted includes a machine learning like C5.0.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.21-30
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• 2021

Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• The Mathematical Education
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• v.51 no.2
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• pp.131-144
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• 2012

The purpose of this study is to investigate the change of mathematics pre-service teachers' teaching behaviors in microteaching. This study is organized along the following lines: 1) mathematics pre-service teachers conduct twice microteachings, 2) the microteaching recordings and lesson observation reports written by pre-service teachers are analyzed. Through reviewing the first microteaching, pre-service teacher have reviewed and found out improvements of their teaching. In the second microteaching, pre-service teachers' teaching behaviors have been positively and effectively changed with respect to teaching methods, proposal of learning objectives, prior knowledge usage, presenting lesson's content, concise descriptions, brief language usages, multimedia, and appropriate questions. However, they frequently used inappropriate expressions from their unconscious habits. Therefore, the educational institutions should provide opportunities involved in well-structured microteaching training program with pre-service teachers, which in turn, help pre-service teachers to have more positive teaching competence.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.433-441
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• 2020

This study was conducted to identify the factors that influence the other behaviors taken by nursing students during online lectures. The study subjects were 304 nursing students in three universities. Data were collected between April 20 and 30, 2020, using by completing structured self report questionnaires. Data were analyzed using T-test, ANOVA, Pearson's correlation coefficient, and multiple regression using SPSS 26.0 program. In correlation analysis, significant negative correlations were found between other behaviors, interest(r=-17, p<.01), understanding(r=-19, p<.01), needs(r=-12, p<.05), learning motivation(r=-12, p<.05), self-regulation efficacy(r=-11, p<.05), learning confidence(r=-14, p<.05), lecture satisfaction(r=-22, p<.01), lecture flow(r=-24, p<.01). In the multiple regression analysis, learning confidence, prefer to discuss & present (β=.19), lecture flow(β=-.15), lecture satisfaction(β=-.15) were statistically significant factors that explained 10.6% of variance of other behaviors taken by nursing students during online lectures. Thus, we suggest to develop that teaching methods and programs to reduce other behaviors taken by nursing students during online lectures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.151-156
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• 2005

This paper proposes a Distributed Autonomous Robotic System(DARS) based on an Artificial Immune System(AIS) and a Classifier System(CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in given environment. These actions are composed of two types: aggregation and dispersion. AIS decides one among these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local one. The proposed system will be more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• Educational Technology International
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• v.15 no.2
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• pp.143-170
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• 2014

As it was argued that students' cognitive engagement can be, at least in part, modified by individual or learning environmental factors, prior studies have attempted to identify the factors explaining the variability of students' cognitive engagement. This literature review has shown that students' cognitive engagement can be altered by various elements in the learning environment design such as factors related students' perceptions of teaching quality, characteristics of tasks and learning activities, teachers' behaviors during instruction, classroom goal structures, the integration of student oriented learning, action learning, problem-based learning, and constructivist learning, and academic disciplines. Based on the review, this study suggests that more studies are required to focus on understandings how the integration of instructional design principles into courses and the levels of student cognitive engagement in these courses are related. Also, an investigation of direct and indirect effect of learning environments taking into account students' personal factors would provide a more accurate picture of the relationship between learning environmental factors and students' cognitive engagement.