• The Journal of Korean Association of Computer Education
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• v.20 no.4
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• pp.47-57
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• 2017

The purpose of this study is to verify the effectiveness of a CT-CPS(Computational Thinking-based Creative Problem Solving) instructional model on the cognitive and the affective area of middle school students' software class. To achieve our goal, we explored theoretical background and designed a lesson plan based on CT-CPS instructional model. Then we analyzed our experimental results after applying the lesson plan to middle school students. We performed our experiment to an experimental group by using our CT-CPS instructional model-based lesson plans, and we carried out three pre and post tests about cognitive and affective area, i.e. creative problem solving ability, meta cognition and motivation of learning. As a result, most of the test factors were statistically improved, so the effectiveness of the CT-CPS instructional model on the cognitive and the affective area of middle school students' software class was verified.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.37-59
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• 2004

Team decision making is a collective behavior that needs to be understood by considering properties belonging to team and individual member domains together. This paper introduces a conceptual model called "Dual-Level(DL)" model that describes a team decision-making process in terms of team level, member level, and the relationship between them. The team-level view explains the decision-making process by considering the team as a wholeand divides the process into three stages: Problem Conceptualization, Alternative Generation, and Selection. The member-level view describes what happens to individual members when they go through the group process and splits it into the five phases: Individual Cognitive Mapping, Problem Decomposition, Subproblem Session, Subproblem Integration, and Team Decision. The DL model works as a theoretical framework to explore team decision making by using a set of computational models of team design and team members. In practice, the conceptual framework is used to build a computational model of decision making team, called "Team-Soar."

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.129-136
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• 2014

One of the fundamental issues in the design of dynamic spectrum access policy is the modeling of the dynamic behavior of channel occupancy by primary users. Under a Markovian modeling of channel occupancy, a periodic sensing and greedy access policy is known as one of the simple and practical dynamic spectrum access policies in cognitive radio networks. In this paper, the primary occupancy of each channel is modeled as a discrete-time alternating renewal process with generally distributed on- and off-periods. A periodic sensing and greedy access policy is constructed based on the general channel occupancy model. Simulation results show that the proposed policy has better throughput than the policies using channel models with exponentially distributed on- or off-periods.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.303-321
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• 2006

According to Harris'(1966) distributional hypothesis, understanding the meaning of a word is thought to be dependent on its context. Under this hypothesis about human language ability, this paper proposes a computational model for native speaker's language processing mechanism concerning word sense disambiguation, based on two sets of experiments. Among the three computational models discussed in this paper, namely, the logic model, the probabilistic model, and the probabilistic inference model, the experiment shows that the logic model is first applied fer semantic disambiguation of the key word. Nexr, if the logic model fails to apply, then the probabilistic model becomes most relevant. The three models were also compared with the test results in terms of Pearson correlation coefficient value. It turns out that the logic model best explains the human decision behaviour on the ambiguous words, and the probabilistic inference model tomes next. The experiment consists of two pans; one involves 30 sentences extracted from 1 million graphic-word corpus, and the result shows the agreement rate anong native speakers is at 98% in terms of word sense disambiguation. The other pm of the experiment, which was designed to exclude the logic model effect, is composed of 50 cleft sentences.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.211-217
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• 2011

This paper proposes a quasi-optimal linear DOA (Direction-of-Arrival) estimator which is necessary for the development of a real-time robot auditory system tracking moving acoustic source. It is well known that the use of conventional nonlinear filtering schemes may result in the severe performance degradation of DOA estimation and not be preferable for real-time implementation. These are mainly due to the inherent nonlinearity of the acoustic signal model used for DOA estimation. This motivates us to consider a new uncertain linear acoustic signal model based on the linear prediction relation of a noisy sinusoid. Using the suggested measurement model, it is shown that the resultant DOA estimation problem is cast into the NCRKF (Non-Conservative Robust Kalman Filtering) problem [12]. NCRKF-based DOA estimator provides reliable DOA estimates of a fast moving acoustic source in spite of using the noise-corrupted measurement matrix in the filter recursion and, as well, it is suitable for real-time implementation because of its linear recursive filter structure. The computational efficiency and DOA estimation performance of the proposed method are evaluated through the computer simulations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.1
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• pp.41-60
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• 1998

This paper derives a consistent and bias corrected extension of Akaike's Information Criterion (AIC), $AIC_{bc}$, based on Kullback-Leibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of the asymptotically efficient model selection criteria for small and large samples will be overcome. The $AIC_{bc}$ also provides a consistent model order selection. Thus, it is widely applicable to data with small and/or large sample sizes, and to cases where the number of free parameters is a relatively large fraction of the sample size. Relationships with other model selection criteria such as $AIC_c$ of Hurvich, CAICF of Bozdogan and etc. are discussed. Empirical performances of the $AIC_{bc}$ are studied and discussed in better model order choices of a linear regression model using a Monte Carlo experiment.

• Korean Journal of Cognitive Science
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• v.24 no.2
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• pp.95-109
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• 2013

With the rapid evolution of the personal device environment, the demand for natural language applications is increasing. This paper proposes a morpheme segmentation and part-of-speech tagging model, which provides the first step module of natural language processing for many languages; the model is designed for mobile devices with limited hardware resources. To reduce the number of morpheme candidates in morphological analysis, the proposed model uses a method that adds highly possible morpheme candidates to the original outputs of a conventional left-longest-match-preference method. To reduce the computational cost and memory usage, the proposed model uses a method that simplifies the process of calculating the observation probability of a word consisting of one or more morphemes in a conventional hidden Markov model.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.499-511
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• 2019

The preceding study of this study developed puzzle-based computer science education programs to enhance the computational thinking of elementary school students over 1 to 3 times. The preceding study then applied such programs into the field, categorized the effects of education into CT creativity and CT cognitive ability to improve the education programs. Based on the results of these preceding studies, the hierarchical Bayesian inference modeling was performed using age and CT thinking ability as parameters. From the results, this study predicted the effectiveness of puzzle-based computer science education programs in middle and high schools and proposed major improvement areas and directions for puzzle-based computer science education programs that are to be deployed in the future throughout middle and high schools.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.89-91
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• 2007

본 논문은 인간의 어휘획득(Lexical Aquisition)과정을 하이브리드(hybrid)한 형태의 계산주의적(Computational) 모델을 설계,반복 실험을 통해 인지신경기반의 어휘습득 모델을 구현하고 실험하였다. 이 연구를 통해 인간의 어휘획득 과정을 모사(simulate)할수 있었고, 이로인해 인지신경기반 어휘 정보처리 시스템 개발을 위한 자동어휘 획득, 심성 어휘집 표상, 어휘 인식(word recognition)의 계산주의적 모델 개발에 기여할 수 있을 것이다.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.379-390
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• 2020

Recently, interest in online teaching·learning and evaluation tools has increased in the context of Covid-19. In order to use tools effectively, it is necessary to identify the structural influence and causal relationship between the learner's affective and cognitive variables. In this study, to identify a causal relationship between motivation and thinking while using online judge, research and competing model were established and model fit/path analysis were performed. It was found that there was a linear causal relationship from tool usage, self-efficacy, flow, logical thinking, to computational thinking. It was confirmed that 'self-efficacy → flow', or 'flow' had mediating effect on the path from tool usage to thinking, and tool usage was not exerted to thinking through 'flow → self-efficacy'. The causality of 'logical thinking → computational thinking' was identified on the path where tool usage affects thinking ability through learning motivation, but the causality of 'computational thinking → logical thinking' was not identified.