• 한국콘텐츠학회논문지
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• 제10권9호
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• pp.469-477
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• 2010

일반학교와 특수학교에서 시행하고 있는 컴퓨터 교육 과정 내용과 수업 방식을 조사 분석하고, 일반학교 비장애 학생들의 교수-학습법을 참고하여 특수학교 지체장애 학생들에게 새로운 교수-학습법을 제시하고 수행하였다. 제안된 교수-학습법에 대한 지체학생들의 수업 방식, 학생들의 태도를 평가하고 기존 교수-학습법과 제안된 교수-학습법의 차이점과 그 결과를 분석하여 제안된 특수학교 컴퓨터 교육에 대한 교수-학습법의 효율성을 제시하였다.

• 공학교육연구
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• 제15권5호
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• pp.54-59
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• 2012

It is reported that the constructivistic learning method (CLM) enhances the understanding of the students in the learning process, especially in engineering classes. In CLM-based classes, the students can take the initiative in the learning process, which is called the student-centered model of the learning process. This is different from the traditional learning method based on the teacher-centered model, where a teacher plays the central role in the learning process of students. The authors have applied the method of CLM to one of the Engineering classes, namely production planning and inventory control (PPIC) class for undergraduate students. The PPIC class provides multimedia-based study materials and factory visits as well as regular lecture sections to cover the whole subject of inventory control theory and practice. In the review sessions, students are divided into several groups, and question-and-answer discussions were actively carried out among these groups under the support of the teacher as a facilitator. It was observed that the student engagement in the class was very active compared to the conventional lecture-based classes. As for further support of students understanding on the subject, simulation-based materials are also under study for the class. This paper presents the review of case study of CLM-based PPIC class and discusses the feasibility of simulation-based study materials for further improvement of the class.

• 문화기술의 융합
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• 제8권6호
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• pp.553-560
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• 2022

고등교육기관의 역량 중심 교육과정 운영을 위해서는 교과목 수준에서 교과 학습목표(성과기준)의 성취수준을 다각도로 평가하여 학습자의 역량 함양 정도를 파악하는 교과기반 학습평가 방법에 대한 연구가 지속적으로 필요하다. 본 연구에서는 교과목 학습성과, 학습주제, 학습개념 기반의 학습평가 모델 및 성취수준에 따른 개인화된 학습 피드백 모델을 제안한다. 먼저 데이터 모델링 과정에서 교과목의 계층화된 학습성과, 학습주제 및 학습개념 그래프 및 학습성과-평가 매트릭스 모델을 정의하고 이를 기반으로 학습성과별, 학습주제별, 학습자별 등 다각도의 학습성취 수준을 측정하고 피드백하는 알고리즘을 제안한다. 제안한 학습성취평가 모델의 유효성을 검증하기 위해 자바프로그래밍 교과목에 적용하여 실제 데이터를 기반으로 실험을 진행하였으며 그 결과 성취수준의 산출 및 학습 피드백이 가능함을 보였다.

• 한국IT서비스학회지
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• 제10권3호
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• pp.61-76
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• 2011

The purpose of this paper was to design and implement effective learning model for internet ethics education, following the learning principle and procedure of PBL(Problem-Based Learning) which is one of the constructivism teaching-learning theories(, and to apply it). In this learning model, online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals and the main module for online learning run on Moodle, an open source LMS(Learning Management System). It is possible for learner to participate actively in creation of micro-contents and reorganize contents using various SNS(Social Network Service). The learner can achieve the learner-oriented learning and select micro-contents in order to reorganize the personalized learning contents to take advantage of SNS among learners. To examine educational effectiveness of the proposed learning model, an experimental study was conducted through the education content and method to the subjects of two classes in the second-grade of university located in OO city. 60 students(treatment group=30, control group=30) participated in the experiment. The result statistically verified that the proposed learning method is more effective in cultivating consciousness of internet ethics than the face-to-face PBL learning method. The results of this paper also showed that a lecture using blended learning is efficient in achieving learning performance and that learners responded positively(, which are indicating that the higher effectiveness of learning would be expected) by forming connectedness among learners using SNS. The results of this paper showed that a lecture using blended learning is effectiveness in achieving learning performance and that learners responded positively, which are indicating that the higher effectiveness of learning would be expected by forming connectedness among learners using SNS.

• 컴퓨터교육학회논문지
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• 제9권2호
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• pp.47-55
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• 2006

효과적인 데이터베이스 수업방법을 모색하기 위하여 상황학습 이론을 교수 학습에 적용하여 효과를 분석하여 보았다. 이를 위해 관련 선행연구를 분석하고 데이터베이스 핵심 내용을 블룸의 교육목표 분류 기준에 의해 분석하였다. 그리고 데이터베이스 교육 내용을 기본 지식 단계와 기능 및 확장지식 단계로 분류하는 전략을 제시하였다. 이와 같은 연구를 바탕으로 실험, 통제 집단을 선정하였으며 데이터베이스 교수학습 효과의 기준을 지식 및 기술과 태도영역으로 설정하였다. 그리고 실제 교육과정을 구성하여 교수학습을 전개하여 다음과 같은 결과를 도출하였다. 적용결과는 상황학습을 적용하여 데이터베이스 교육을 받은 학습 집단이 DB개념, DB 조작, DB테이블 작성에 대해 높은 성취도를 나타내었으며 기능 중심의 데이터베이스 교육을 받은 학습 집단보다 긍정적 태도를 나타냈다. 또한, 향후에는 데이터베이스 및 컴퓨터과학 영역에 대하여 상황학습 이외의 다양한 교수학습 방법을 적용, 분석해야 할 것이다.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제27권2호
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• pp.15-24
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• 2021

Purpose: This study is to present basic data on the Architectural planning of the college of medicine by arranging the functional zoning planning, functional unit planning, and circulation planning, based on the diversification of medical education and the changes of Learning spaces. Methods: This study was conducted by literature review on existing medical education method and learning space planning. And then on-site surveys and questionnaire were conducted on existing facilities. Results: The diversification of medical education has already been progressing for a long time, and the development of information technology is integrated into the learning space, and the evolution of the learning method and the flexibility of the learning space give effect to the change of the learning space. ① This study reconstructed the existing classification method of university facilities into the fuctional zoning method that combines the architectural concept of function and movement. ② The functions and functional units of the college of medicine according to the proposed functional zone were arranged, and the space required for the medical college was suggested. ③ The function and type of movement of medical college can be classified into professor/student fusion type, teaching and research separation type, and learning and learning shared space integrated arrangement type. In the future, it is necessary to consider fusion type or integrated arrangement type rather than separation type. Implications: the evolution of the learning method and the flexibility of the learning space give effect to the change of the learning space.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.633-640
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• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.135-138
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• 2002

This paper addresses incremental learning method for regression. SVM(support vector machine) is a recently proposed learning method. In general training a support vector machine requires solving a QP (quadratic programing) problem. For very large dataset or incremental dataset, solving QP problems may be inconvenient. So this paper presents an incremental support vector learning method for function approximation problems.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.