• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.594-602
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• 2021

This study aims to identify the learning behavior patterns recognized by students to find effective tutoring operational methods, and further analyze the impact of learning behavior patterns on academic performance and learning satisfaction. To this end, 105 participants in the tutoring program at D University based in Busan Metropolitan City collected data and conducted descriptive statistics, correlation analysis and regression analysis according to research problems. First, the study found that students who participated in tutoring had the most environment-dependent and self-taught learning behavioral styles and environment-independent and self-taught learning behavioral style. Second, the correlation between learning behavioral styles and academic achievement and learning satisfaction shows that there is a high correlation between positive and cooperative learning behavioral styles and environment-independent and self-taught learning behavioral styles. Third, regression analysis on academic achievement and learning satisfaction showed that positive and cooperative learning behavioral styles significantly affects learning satisfaction, but environment-independent and self-taught learning behavioral style, environment-dependent and self-taught learning behavioral style, and passive learning behavioral style were not significant. These results suggest that from the school perspective, learning behavior can be recognized as an important factor in students' academic success and failure, so instructors need to check learners' learning behavior patterns and provide appropriate tutoring teaching and learning design plans.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.574-599
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• 2018

The purpose of this study is to suggest implications for the importance of shared leadership of leaders by analyzing the influence of learning orientation and unlearning on the recognition of leader's shared leadership and employees'. The questionnaire survey was conducted on the employees who work as knowledge workers in the domestic SMEs. A total of 387 questionnaires were collected using SPSS 24.0 statistical package. The results of this study were that the relationships between a leader's shared leadership and innovation behavior, shared leadership and learning orientation, and learning orientation and innovation behavior were positive. In addition, learning orientation mediated in the relationship between shared leadership and innovation behavior, and unlearning reinforced the relationship between shared leadership and learning orientation. The implication of this study is that the employees themselves need continuous reinforcement activities for active unlearning and learning orientation in order to improve the innovation behavior of the employees. In addition, the shared leadership of leaders in employees and organization is more important.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.131-137
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• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.245-254
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• 2022

This study is a study to empirically verify the mediating effect on college life adaptation and career preparation behavior of college students. The purpose of this study is to empirically analyze the multi-mediated effects of major satisfaction, job stress, and self-directed learning. For this study, 216 university students were enrolled. For the collected data, SPSS Process macro was used. The result is as follows. First, there were multiple parallel mediating effects and multiple serial mediating effects on major satisfaction, job stress, and self-directed learning between college life adaptability and career preparation behavior. Second, the path of simple mediation and double mediation effect was found between college life adaptation and career preparation behavior. Based on the research, the necessity of revitalizing the program for revitalization of teaching activities and industry-academic cooperation activities in the major field and improvement of career preparation behavior and university life adaptation ability and follow-up research were suggested.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.487-508
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• 2024

This study aims to empirically analyze the effects of the learning organization characteristics in public libraries on the innovative work behavior of librarians. In this analysis, 113 librarians from 15 public libraries in the Seoul Metropolitan Area of South Korea were surveyed to investigate the learning organization characteristics of libraries and innovative work behavior. Through a multiple regression analysis of learning organization characteristics and innovative work behavior, it was found that, among the sub-factors of learning organization characteristics, creating continuous learning opportunities and creating systems to capture and share learning had a positive effect on idea realization among the sub-factors of innovative work behavior. From this, it was interpreted that public libraries should increase financial and non-financial support for librarians to learn and also that libraries should create various systems such as electronic bulletin boards and meetings in which librarians can share their learning. Moreover, the sub-factors of learning organization characteristics were found to have no effect on idea generation and idea promotion among the sub-factors of innovative work behavior, which indicated that it will be necessary to identify the organization characteristics that affect idea generation and idea promotion. This study is significant in that it identified the sub-factors of learning organization characteristics that promote the innovative work behavior of public library librarians.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.59-76
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• 2008

In this paper, we propose an agent architecture called L-CAA that is quite effective in real-time dynamic environments. L-CAA is an extension of CAA, the behavior-based agent architecture which was also developed by our research group. In order to improve adaptability to the changing environment, it is extended by adding reinforcement learning capability. To obtain stable performance, however, behavior selection and execution in the L-CAA architecture do not entirely rely on learning. In L-CAA, learning is utilized merely as a complimentary means for behavior selection and execution. Behavior selection mechanism in this architecture consists of two phases. In the first phase, the behaviors are extracted from the behavior library by checking the user-defined applicable conditions and utility of each behavior. If multiple behaviors are extracted in the first phase, the single behavior is selected to execute in the help of reinforcement learning in the second phase. That is, the behavior with the highest expected reward is selected by comparing Q values of individual behaviors updated through reinforcement learning. L-CAA can monitor the maintainable conditions of the executing behavior and stop immediately the behavior when some of the conditions fail due to dynamic change of the environment. Additionally, L-CAA can suspend and then resume the current behavior whenever it encounters a higher utility behavior. In order to analyze effectiveness of the L-CAA architecture, we implement an L-CAA-enabled agent autonomously playing in an Unreal Tournament game that is a well-known dynamic virtual environment, and then conduct several experiments using it.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.572-587
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• 2020

Experience API provides a learner-centered model for learning data collection and learning process recording. In particular, it can record learning data from multiple data sources. Therefore, Experience API provides very good support for ubiquitous learning. In this paper, we put forward the architecture of ubiquitous learning system and the method of reading the learning record from the ubiquitous learning system. We analyze students' learning behavior from two aspects: horizontal and vertical, and give the analysis results. The system can provide personalized suggestions for learners according to the results of learning analysis. According to the feedback from learners, we can see that this u-learning system can greatly improve learning interest and quality of learners.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the 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 has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.591-597
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• 2007

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.