• Title/Summary/Keyword: Behavior learning

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The mediating role of team learning behavior between team efficacy and team innovative performance in R&D team (연구개발팀에서 팀 효능감과 팀 혁신성과간의 관계에서 팀 학습행동의 매개역할)

  • Lee, Jun Ho;Kim, Hack Soo
    • Knowledge Management Research
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    • v.13 no.3
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    • pp.105-125
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    • 2012
  • Previous studies have focused on individual and organizational learning. Amid an increasingly complex business environment, a team system designed to improve flexibility and adaptability constitutes the most basic part of an organization. Still, team learning has rarely been discussed. In addition, team learning behavior, despite being an important part of a team process, is often mentioned as a team-level outcome variable. Given that team learning behavior involves constant changes in thinking and behavior, a shared belief among team members is needed in order to positively influence innovative performance of a team. In spite of that, there has been only limited discussion of it. Besides, few domestic studies have dealt with R&D teams that can clearly demonstrate team learning behavior and team innovative performance. This study is an empirical analysis of the impact of team efficacy on team innovative performance and the mediating role of team learning behavior based on materials collected from team leaders and their immediate subordinates in 268 R&D teams. The analysis showed that team learning behavior actually has a positive effect on team innovative performance. Team efficacy also turned out to have a positive influence on team learning behavior. Lastly, the study found that team learning behavior played a mediating role in the relationship between team efficacy and team innovative performance. Based on those results, the study has identified implications and suggested directions for future research.

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The Effects of Learning Participation Motivation and Self-Efficacy for Group Work on Knowledge Sharing Behavior in Online Learning Environment (온라인 학습환경에서 학습참여동기와 협력적 자기효능감이 지식공유행동에 미치는 영향)

  • Park Hyejin;Cha, Seungbong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.3
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    • pp.105-115
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    • 2023
  • This study analyzed the effects of learning participation motivation and collaborative self-efficacy on knowledge sharing behavior in an online learning environment. Collaborative learning in the online learning environment took the initiative in team formation, learning topic selection, learning planning and execution, and reflection. Collaborative learning was operated as an extracurricular program, and a survey was conducted targeting students who finally completed all learning activities. The results of the study are as follows. First, goal-oriented motivation and self-Efficacy for group work, showed significant influence on knowledge sharing behavior. Second, activity-oriented motivation did not show a statistically significant effect relationship. Interpreting the analysis results, it can be judged that the higher the goal-oriented motivation and self-Efficacy for group work of students who performed collaborative learning in an online learning environment, the higher the willingness to share knowledge, skills, and information they know. This study explored the outcomes of collaborative learning conducted in an online learning environment. It is meaningful that the learner's learning participation motivation was identified and the effect of self-Efficacy for group work, which can be expressed in collaborative learning situations, on knowledge sharing behavior, which is a necessary behavior for group performance, is significant.

Online Evolution for Cooperative Behavior in Group Robot Systems

  • Lee, Dong-Wook;Seo, Sang-Wook;Sim, Kwee-Bo
    • International Journal of Control, Automation, and Systems
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    • v.6 no.2
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    • pp.282-287
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    • 2008
  • In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

Learning Activities and Learning Behaviors for Learning Analytics in e-Learning Environments

  • Jin, Sung-Hee;SUNG, Eunmo;Kim, Younyoung
    • Educational Technology International
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    • v.17 no.2
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    • pp.175-202
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    • 2016
  • Most of the learning analytics research has investigated how quantitative data can affect learning. The information that is provided to learners has been determined by teachers and researchers based on reviews of the previous literature. However, there have been few studies on standard learning activities that are performed in e-learning environments independent of the teaching methods or on learning behavior data that are obtained through learning analytics. This study aims to explore the general learning activities and learning behaviors that can be used in the analysis of learning data. Learning activities and learning behavior are defined in conjunction with the concept of learning analytics to identify the differences between teachers' and learners' learning activities. Learning activities and learning behavior were verified by an expert panel review in an e-learning environment. The differences between instructors and learners in their usage were analyzed using a survey method. As results, 8 learning activities and 29 learning behaviors were validated. The Research has shown that instructors' degree of utilization is higher than that of the learners.

The Effect of Shared Leadership perceived by organizational members on Team Learning Behavior and Team Effectiveness

  • Moon Jun Kim;Taek Keun
    • International journal of advanced smart convergence
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    • v.13 no.1
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    • pp.152-161
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    • 2024
  • The purpose of this study sought to determine the impact of shared leadership perceived by organizational members on team effectiveness and team learning behavior. For this purpose, the results of the empirical analysis of 206 organizational members are as follows. First, shared leadership was analyzed to improve team effectiveness. Second, shared leadership had a positive effect on team learning behavior. Third, team learning behavior was statistically significantly analyzed for team effectiveness. This study confirmed the importance of shared leadership, which has a positive impact on team effectiveness and team learning behavior. This may require building a new culture that can demonstrate the inherent leadership of organizational members in the influence relationship between shared leadership, team effectiveness, and team learning behavior. In other words, in order to systematically demonstrate and implement shared leadership, the execution ability of executives, managers, and working-level managers is important. To this end, it is necessary to build an organizational culture that matches the characteristics of the organization and develop and continuously implement human resource development systems and programs that can implement this.

A Study of Video-Based Abnormal Behavior Recognition Model Using Deep Learning

  • Lee, Jiyoo;Shin, Seung-Jung
    • International journal of advanced smart convergence
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    • v.9 no.4
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    • pp.115-119
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    • 2020
  • Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

The Effects of Undesirable Parenting Behavior, Children's Peer Relationship and Self-regulated Learning on Children's Self-esteem (부모의 바람직하지 않은 양육행동과 아동의 친구관계 및 자기조절학습능력이 아동의 자아존중감에 미치는 영향)

  • Woo, Sujung
    • Korean Journal of Human Ecology
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    • v.23 no.5
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    • pp.759-771
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    • 2014
  • The purpose of this study was to examine the effects of undesirable parenting behavior, children's peer relationship and self-regulated learning on children's self-esteem. Using the data from Korean Children and Youth Panel Survey, this study was conducted with Structural Equation Modeling(SEM). The results of this study were as follows. First, parents' undesirable parenting behavior influenced directly on children's self-esteem, and peer relationship. Second, children's peer relationship influenced directly on self-regulated learning, and self-esteem. Third, children's self-regulated learning influenced directly on self-esteem. Fourth, parents' undesirable parenting behavior did not influenced directly on children's self-regulated learning. But children's peer relationship and self-regulated learning had mediating effects on the relationship between undesirable parenting behavior and children's self-esteem.

The Roles of Market-Based Learning and Customer Orientation in Shaping Effective Selling Behavior and Efforts

  • Park, Jeong Eun;Kim, Seongjin;Lee, Sungho
    • Asia Marketing Journal
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    • v.11 no.2
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    • pp.37-51
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    • 2009
  • Although previous studies have made significant progress in adaptive selling behavior (ASB), few studies have considered salesperson's customer orientation (CO) and learning behavior as determinants of effective sales management (ASB and relationship-making efforts), despite the discussion of important roles of these constructs. The authors test not only the relationships of salesperson's CO and market-based learning behavior to ASB and relationship-making efforts, but also the effects of ASB on relationship-making efforts and performance. The results of the study, which is done with samples of salespeople from Korean companies, indicate that salesperson's CO and market-based learning behavior are identified as significant determinants of ASB. Moreover, both salesperson's ASB and relationship-making efforts have significant effects on sales performance. On the other hand, as per salesperson's relationship-making efforts, salesperson's CO has a positive effect, but salesperson's market-based learning behavior and ASB do not influence his or her relationship-making efforts, which suggest a provocative possibility of conceptualization regarding the relationship between ASB and relationship management efforts.

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Moderating Effects of Parental Monitoring in the Relationship between Children's Dependency on Mobile Phones and Control of Learning Behavior (아동의 휴대전화 의존과 학습행동 통제 간의 관계에서 부모감독의 조절효과)

  • Cho, Yoonju
    • Journal of the Korean Home Economics Association
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    • v.51 no.2
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    • pp.253-261
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    • 2013
  • The purpose of this study was to investigate the moderating effects of parental monitoring on the relationship between children's dependency on mobile phones and control of learning behavior. The data came from the 2010 Korean Children and Youth Panel (N = 1,609) conducted by the National Youth Policy Institute. The analysis method used was Structural Equation Modeling by using SPSS 17.0 and AMOS 7.0. To test the significant moderating effects, Ping's two-step technique, which is free from the requirement of nonlinear constraints, was used. Our results demonstrated that children's dependency on mobile phones had negative effects on control of learning behavior, and the interaction effects between such dependency and parental monitoring affected the control of learning behavior. Thus, these results proved the moderating effects of parental monitoring in the control of learning behavior. This study suggests that parental monitoring buffers against having difficulties to control and adjust one's behavior associated with control of learning behavior, which is affected by the dependency on mobile phones among children. We discussed that the risks of children's dependency on mobile phones and parental monitoring should be acknowledge as a significant protective factor.

Multi Behavior Learning of Lamp Robot based on Q-learning (강화학습 Q-learning 기반 복수 행위 학습 램프 로봇)

  • Kwon, Ki-Hyeon;Lee, Hyung-Bong
    • Journal of Digital Contents Society
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    • v.19 no.1
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    • pp.35-41
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    • 2018
  • The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.