• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.4
• /
• pp.11-18
• /
• 2021

We propose a method that combines learning in a virtual environment and a real environment for indoor autonomous driving through reinforcement learning. In case of learning only in the real environment, it takes about 80 hours, but in case of learning in both the real and virtual environments, it takes 40 hours. There is an advantage in that it is possible to obtain optimized parameters through various experiments through fast learning while learning in a virtual environment and a real environment in parallel. After configuring a virtual environment using indoor hallway images, prior learning was carried out on the desktop, and learning in the real environment was conducted by connecting various sensors based on Jetson Xavier. In addition, in order to solve the accuracy problem according to the repeated texture of the indoor corridor environment, it was possible to determine the corridor wall object and increase the accuracy by learning the feature point detection that emphasizes the lower line of the corridor wall. As the learning progresses, the experimental vehicle drives based on the center of the corridor in an indoor corridor environment and moves through an average of 70 steering commands.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.12
• /
• pp.5486-5492
• /
• 2011

This paper presents a model of ubiquitous learning environment system based on the concepts of ubiquitous computing technology that enables learning to take place anywhere at anytime. This ubiquitous learning environment is described as an environment-friendly learning scheme that supports students' learning using digital media in geographically distributed environments. The u-learning model is a web-based e-learning system that could enable learners to acquire knowledge and skills through interaction between them and the ubiquitous learning environment. Education is happening all around the student but the student may not even be conscious of the learning process. Source data is present in the embedded objects and students do not have to do anything in order to learn. The communication between devices and the embedded computers in the environment allows learners to learn in an environment of their interest while they are moving, hence, attaching them to their learning environment.

• Asia pacific journal of information systems
• /
• v.16 no.1
• /
• pp.1-21
• /
• 2006

e-Learning can be seen as not only one of Internet-based information technologies which can provide education services but also one of teaching-learning methods which can implement self-directed learning. Thus, for evaluation of e-Learning effectiveness, both information-technology-based learning environment and learners' abilities in self-learning and computer-using should be considered simultaneously. This study suggests a research model for evaluating the effectiveness of e-Learning, which is theoretically based on information systems success model, constructivism and self-efficacy. The model is composed of three parts: effectiveness, learning environment, and learners' self-efficacy. Effectiveness is a part of dependent variables: satisfaction and academic performance. Learning environment and learners' self-efficacy can be considered as two sets of explanation variables for effectiveness. The former consists of learning management system, learning contents, and interactions that are provided bye-Learning and the latter means learners' self-regulated efficacy and computer self-efficacy. We show validity of the model empirically by surveying the college students who have experienced e-Learning. In result, most of all hypotheses suggested in this model are accepted in low significant level.

• Korean Journal of Childcare and Education
• /
• v.15 no.5
• /
• pp.113-138
• /
• 2019

Objective: This study aimed to determine the effect of a teacher professional learning environment at workplace day care centers on the professionalism of beginning teachers and also to verify the mediating effects of the beginning teachers'work task motivation on that relationship. Methods: The participants were 129 new teachers currently employed at workplace day care centers. The Current Status for Professional Competence was used to measure the teachers' professionalism. The Professional Learning Environment Inventory assesses the professional learning environment of day care centers. To measure the work task motivation, the Work Tasks Motivation Scale for Teachers was used. Results: Firstly, positive relationships were identified between a professional learning environment and their intrinsic motivation and identified motivation. However, there were also negative relationships between the professional learning environment and their introjected motivation and amotivation. Secondly, this study partially verified the mediating effects of the beginning teachers'work task motivation on the relationship between the professional learning environment and teachers' professionalism. Conclusion/Implications: The relationship between the professional learning environment and the professionalism of beginning teachers was mediated by the teachers'own intrinsic motivations, identified motivations, and amotivation.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.2
• /
• pp.345-353
• /
• 2006

Over the past few years, training professionals have become more pragmatic in their approach to technology-based media by using it to augment traditional forms of training delivery, such as classroom instruction and text-based materials. This trend has led to the rise of the term blended learning. Blended learning, an environment of e-learning, is a powerful learning solution created through a mixture of face-to-face and online learning delivered through a mix of media and superior learning experiences. In this article we design and implement an e-learning environment for blended learning. The environment focused on following factors: learning activity and participation of learners, and real time feedback of instructor.

• Knowledge Management Research
• /
• v.9 no.4
• /
• pp.17-33
• /
• 2008

This study proposes University 2.0 as a model case of open, flexible, and distributed learning environment for higher education based on theoretical foundations and perspectives. As web 2.0 technologies emerge into the field of education, ways of generating and disseminating information and knowledge have been drastically changed. Professors are no longer the only source of knowledge. Students using internet often become prosumers of knowledge who search and access information through the web as well as publish their own knowledge using the web. A concept and framework of University 2.0 is introduced for implementing the new interactive learning paradigm with an open, flexible and distributed learning environment for higher education. University 2.0 incorporates online and offline learning environments with various educational media. Furthermore, it employs various learning strategies and integrates formal and informal learning through learning communities. Both instructors and students in University 2.0 environment are expected to be active knowledge generators as well as creative designers of their own learning and teaching.

• Educational Technology International
• /
• v.13 no.1
• /
• pp.101-144
• /
• 2012

Students mostly precede their learning without any direct support of instructor in e-learning and mobile educational environment. Many useful strategies and tools to facilitate self-regulated learning behaviors in e-learning environment have been introduced, yet, the limit has been reached by only suggesting self-regulated strategies with visual information in the most researches. Accordingly, this research is intent to propose the sound design strategies that facilitate learner's self-regulated learning behaviors in mobile learning environment. To achieve the objective of the research, two research questions are presented. First, what are the sound design strategies that facilitate the self -regulated learning behaviors in mobile learning environment?. Second, what are the results of evaluating the developed sound design strategies in terms of facilitating self-regulated learning behaviors?. To solve these research questions, the literature reviews on characteristics of mobile learning, concepts and features of self-regulated learning and sound were done to establish the sound design strategies. Through formative research method targeting instructional designers, sound design strategies were modified and supplemented. The research to validate these was performed and to verify the effect of the derived sound design strategies, the usability test aimed at instructional designer and learners was conducted. The final sound design strategies through this research process were six general design strategies and the sixteen detailed strategies. This research is meaningful because this offers the basic research on sound information design which has been lacking and help upgrade the upper limit of instructional design which mainly focused on visual information in mobile learning environment that shows information in a small screen.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.3
• /
• pp.633-640
• /
• 1999

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like Q-learning and TD(Temporal Difference)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present COMREL(COMpressed REinforcement Learning) algorithm for finding the shortest path fast in a maze environment, select the candidate states that can guide the shortest path in compressed maze environment, and learn only the candidate states to find the shortest path. After comparing COMREL algorithm with the already existing Q-learning and Priortized Sweeping algorithm, we could see that the learning time shortened very much.

• Journal of The Korean Association of Information Education
• /
• v.17 no.3
• /
• pp.243-252
• /
• 2013

In this paper, we analyzed and suggested an educational strategy and features for project-based learning in smart learning environment. Smart learning environment is different of e-learning environment, therefore the educational strategy for smart learning is needed. In this paper, we conducted project-based learning for elementary school students in smart learning environment, and we analyzed the difference between a normal project-based learning and a project based learning in smart learning environment and suggested the educational strategy and the considerations through questionnaire, interview, and observation, Furthermore, we figured out factors that affected educational satisfaction of student. As a result, students thought that there is difference of smart project-based learning in some factors: technology and devices, software, and participation for learning. Also, we found main factors to affect educational satisfaction are two factors: technology and devices, and always-on.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.149.5-149
• /
• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...