• 공학교육연구
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• 제15권2호
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• pp.20-29
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• 2012

직업흥미와 적성에 맞는 일을 하는 것이 개인의 정서적 안정 및 일의 효율성 면에서 긍정적인 영향을 끼친다. 이러한 맥락에서 본 연구는 학업우수 공대학생들의 전공진입에 따른 학업성취, 학습태도, 진로결정 양상을 검토하고자 수행되었다. 이를 위하여 공대학생들을 대상으로 자신이 선택한 전공과 진로와의 연관성을 조사하였고, 연관성이 있는 집단과 그렇지 않은 집단 간에 학업성취, 학습태도, 진로타협 양상이 차이를 보이는지 분석하였다. 그 결과 공대학생의 전공과 진로방향과의 일치정도는 그들의 학업성취와 연관성이 높으며, 실제로 그들의 학습태도는 학업성취동기와 상관성이 높았고, 아울러 미래 학습태도에도 영향을 주는 것으로 나타났다. 이러한 일치 정도는 향후 진로 결정과정에도 영향을 주기 때문에 본 연구 결과는 공대학생의 진로 지도 및 교육적 지원 방안을 모색하는 데 기초 자료를 제공할 것이다.

• 한국과학교육학회지
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• 제27권5호
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• pp.412-420
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• 2007

이 연구에서는 인식론적 신념이 개념변화 과정에 미치는 영향을 인지갈등, 상황흥미, 주의집중, 상태 학습 전략을 고려하여 조사했다. 사전 검사로 인식론적 신념 검사를 실시한 후, 선개념 검사를 통해 밀도에 대한 특정 오개념을 지닌 중학교 1학년 218명을 선발했다. 변칙사례에 대한 반응 검사와 상황흥미 검사를 실시하고, CAI 프로그램을 통해 밀도 개념학습을 진행했다. 사후 검사로 주의집중, 상태 학습전략, 개념 검사를 실시했다. 연구 결과, 인식론적 선념의 요소인 고정된 능력, 빠른 학습, 확실한 지식들 사이에는 밀접한 관련성이 있었으나, 확실한 지식만이 개념이해에 직접적으로 부정적인 영향을 주었다. 이 영향력보다 상대적으로 영향력은 작았지만, 확실한 지식은 직접적으로 또는 상황흥미를 매개로 주의집중에 영향을 줌으로써 개념이해에 긍정적인 영향을 미치기도 했다. 그러나 인식론적 신념이 얀지갈등과 상태 학습전략을 통해 개념이해에 미치는 영향은 매우 작았다.

• 전자통신동향분석
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• 제34권1호
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• International Journal of Contents
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• 제4권2호
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• pp.19-23
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• 2008

This paper presents a novel method to construct a personalized e-Learning system based on dynamic estimations of item parameters and learners' abilities, where the learning content objects are of the same intrinsic quality or homogeneously distributed and the estimations are carried out using IRT(Item Response Theory). The system dynamically connects the test and the corresponding learning procedures. Test results are directly applied to estimate examinee's ability and are used to modify the item parameters and the difficulties of learning content objects during the learning procedure is being operated. We define the learning unit 'Node' as an amount of learning objects operated so that new parameters can be re-estimated. There are various content objects in a Node and the parameters estimated at the end of current Node are directly applied to the next Node. We offer the most appropriate learning Node for a person's ability throughout the estimation processes of IRT. As a result, this scheme improves learning efficiency in web-base e-Learning environments offering the most appropriate learning objects and items to the individual students according to their estimated abilities. This scheme can be applied to any e-Learning subject having homogeneous learning objects and unidimensional test items. In order to construct the system, we present an operation scenario using the proposed system architecture with the essential databases and agents.

• Educational Technology International
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• 제25권1호
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• pp.27-65
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• 2024

The purpose of this study was to identify differences in learning patterns according to content complexity in video-based learning environments and to derive variables that have an important effect on learning achievement within particular learning contexts. To achieve our aims, we observed and collected data on learners' cognitive processes through perceived affordances, using behavioral logs and eye movements as specific indicators. These two types of reaction data were collected from 67 male and female university students who watched two learning videos classified according to their task complexity through the video learning player. The results showed that when the content complexity level was low, learners tended to navigate using other learners' digital logs, but when it was high, students tended to control the learning process and directly generate their own logs. In addition, using derived prediction models according to the degree of content complexity level, we identified the important variables influencing learning achievement in the low content complexity group as those related to video playback and annotation. In comparison, in the high content complexity group, the important variables were related to active navigation of the learning video. This study tried not only to apply the novel variables in the field of educational technology, but also attempt to provide qualitative observations on the learning process based on a quantitative approach.

• 디지털산업정보학회논문지
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• 제7권1호
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• pp.75-84
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• 2011

The e-learning contents offered in the current educational system does not appropriately reflect the needs of actual users in the planning and development phases. Considering this problem, this study sets the following four topics as its research: Stability of e-learning; Obstacles of the applications of e-learning; e-learning contents that users wants to be offered besides lectures; and methods of e-learning, and based on these goals, it aims at determining the 'needs of the users for the promotion of e-learning. As the target of the study, a survey was conducted with 200 students who have experienced taking e-learning classes in four universities located in Eastern Seoul, which have introduced an e-learning system. The data collected from the survey went through data coding and data cleaning processes and were analyzed by year, major, and department using SAS 9 statistics package program. The result of this study showed that developing and offering services of e-learning contents that are customized to students based on their majors and year can become an effective plan for promoting e-learning.

• 한국컴퓨터정보학회논문지
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• 제16권2호
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• pp.93-99
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• 2011

본 논문에서는 강화학습(RL : Reinforcement Learning) 환경 하에서 정책 기울기 값 기법을 사용하는 적응적인 QoS 라우팅 기법을 제안하였다. 이 기법은 기존의 강화학습 환경 하에 제공하는 기법에 비해 기대 보상값의 기울기 값을 정책에 반영함으로써 빠른 네트워크 환경을 학습함으로써 보다 우수한 라우팅 성공률을 제공할 수 있는 기법이다. 이를 검증하기 위해 기존의 기법들과 비교 검증함으로써 그 우수성을 확인하였다.

• 한국영어학회지:영어학
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• 제3권3호
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• pp.375-410
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• 2003

As Snow (1989) and Sternberg (1985) have long argued, learning, and adaptation to the learning environment or classroom context (at the levels of instructional treatment, interventionist focus on form technique, or pedagogic task) is a result of the interaction of context at each of these levels of description with learners' patterns of abilities. In this paper I argue that this is an important area of research for SLA pedagogy, as well as SLA theory development, and I review recent developments in the study of L2 learning conditions; of the abilities contributing to L2 aptitude; and of their interaction with the processes involved in successful classroom learning and practice, and propose a model of ‘multiple aptitudes’ for classroom learning based on these findings.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.533-539
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• 2003

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.