• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.3-8
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• 2002

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter- and intra- relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the algorithms for content-based retrieval for multimedia database systems.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.981-993
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• 2018

Smart healthcare is convergence of ICT and healthcare services, and interdisciplinary research has been actively conducted in various fields. The objective of this study is to investigate trends of smart healthcare research using topic modeling and ego network analysis. Text analysis, frequency analysis, topic modeling, word cloud, and ego network analysis were conducted for the abstracts of 2,690 articles in Scopus from 2001 to April 2018. Topic Modeling analysis resulted in eight topics, Topics included "AI in healthcare", "Smart hospital", "Healthcare platform", "Blockchain in healthcare", "Smart health data", "Mobile healthcare", " Wellness care", "Cognitive healthcare". In order to examine the topic modeling results core deeply, we analyzed word cloud and ego network analysis for eight topics. This study aims to identify trends in smart healthcare research and suggest implications for establishing future research direction.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.59-69
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• 2020

The purpose of this study is to design the framework of evaluation on learner's cognitive skill for artificial intelligence(AI) education through computational thinking. To design the rubric and framework for evaluating the change of leaner's intrinsic thinking, the evaluation process was consisted of a sequential stage with a) agency that cognitive learning assistance for data collection, b) abstraction that recognizes the pattern of data and performs the categorization process by decomposing the characteristics of collected data, and c) modeling that constructing algorithms based on refined data through abstraction. The evaluating framework was designed for not only the cognitive domain of learners' perceptions, learning, behaviors, and outcomes but also the areas of knowledge, competencies, and attitudes about the problem-solving process and results of learners to evaluate the changes of inherent cognitive learning about AI education. The results of the research are meaningful in that the evaluating framework for AI education was developed for the development of individualized evaluation tools according to the context of teaching and learning, and it could be used as a standard in various areas of AI education in the future.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Korean Journal of Social Welfare Studies
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• v.45 no.2
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• pp.233-261
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• 2014

There are two competing models explaining the causal relationship of depression and self-esteem, and they are cognitive vulnerability model and the scar model. Cognitive vulnerability model explains that low self-esteem poses as a risk factor for development of depressive symptoms/depression while the scar model asserts that the experiences of depression scars the cognitive function of individuals, resulting in negative self-perception. This study was set out to test two models on Korean adults, and to identify factors that are associated with depression and self-esteem relationship. The first four waves (wave 1~4) of the Korea Welfare Panel Study (KOWEPS) were used for analyses, and latent growth curve modeling was employed to examine the relationship. The findings show that the relationship was reciprocal, one affecting the growth trajectory of another over a four year period. Furthermore, education, poverty status, health status, and satisfaction with social relationships were found to be significantly associated with both depression and self-esteem trajectories. Implications for practice and theory are provided.

• Journal of the Ergonomics Society of Korea
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• v.29 no.6
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• pp.907-914
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• 2010

Cognitive model, that is cognitive architecture, is the model expressed with computer program to show the process how human solve a certain problem and it is continuously under investigation through various fields of study such as cognitive engineering, computer engineering, and cognitive psychology. In addition, the much extensive applicability of cognitive model usually helps it to be used for quantitative prediction of human Behavior or Natural programming of human performance in many HCI areas including User Interface Usability, artificial intelligence, natural programming language and also Robot engineering. Meanwhile, when a system designed, an usability test about conceptual design of interface is needed and in this case, analysis evaluation using cognitive model like GOMS or ACT-R is much more effective than empirical evaluation which naturally needs products and subjects. In particular, if we consider the recent trend of very short-end term between a previous technology development and the next new one, it would take time and much efforts to choose subjects and train them in order to conduct usability test which is repeatedly followed in the process of a system development and this finally would bring delays of development of a new system. In this study, we predicted quantitatively the human behavior processes which contains cognitive processes for menu selection in touch screen interface through ACT-R, one of the common method of usability test. Throughout the study, it was shown that the result using cognitive model was equal with the result using existing empirical evaluation. And it is expected that cognitive model has a possibility not only to be used as an effective methodology for evaluation of HCI products or system but also to contribute the activation of HCI cognitive modeling in Korea.

• Korean Journal of Child Studies
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• v.37 no.6
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• pp.145-155
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• 2016

Objective: The purpose of this study was to investigate trajectories of child peer interaction and to compare the causal effects of their predictors, including child individual variables (i.e., gender, language ability, and cognitive ability) and teacher variables (i.e., teacher efficacy and teacher-child interaction). Methods: The participants of this study were 263 children and their teachers from the forth to sixth waves of longitudinal data from the Korean Children and Youth Panel Survey by the Korea Institute of Child Care and Education. The data was analyzed using Pearson's correlation and latent growth modeling. Results and Conclusion: The findings of this study are as follows: First, there was a linear decrease in child negative peer interaction over the course of 3 years, and significant individual differences were found in the trajectories (intercept and slope). Second, the predictors had significant casual effects on the trajectories of child negative peer interaction. The trajectories of child negative peer interaction involving girls, higher cognitive ability, and greater teacher-child interaction showed lower degree of intercept and a quicker decrease. Finally, the implications of findings are discussed.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.41-53
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• 2011

Objective: The purpose of this paper is to introduce new research trends of human error and accident modeling and to suggest future promising research directions in those areas. Background: Various methods and techniques have been developed to understand the nature of human errors, to classify them, to analyze their causes, to prevent their negative effects, and to use their concepts during design process. However, it has been reported that they are impractical and ineffective for modern complex systems, and new research approaches are needed to secure the safety of those systems. Method: Six different perspectives to study human error and system safety are explained, and then seven recent research trends are introduced in relation to the six perspectives. The implications of the new research trends and viable research directions based on them are discussed from a cognitive systems engineering point of view. Results: Traditional methods for analyzing human errors and identifying causes of accidents have critical limitations in complex systems, and recent research trends seem to provide some insights and clues for overcoming them. Conclusion: Recent research trends of human error and accident modeling emphasize different concepts and viewpoints, which include systems thinking, sociotechnical perspective, ecological modelling, system resilience, and safety culture. Application: The research topics explained in this paper will help researchers to establish future research programmes.

• Korean Journal of Child Studies
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• v.23 no.6
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• pp.15-32
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• 2002

Child's temperament, cognitive ability, social competence, mother's affective child rearing and positive expression, father's positive expression, and teacher's positive expression were the variables investigated in relation to the structure of children's emotional competence (EC). Subjects were 20 teachers and 236 five-year-old children and their parents. Data were analyzed by LISREL (Linear Structural Relations), a statistical program for structural equation modeling. Results showed that boys' social competence and mother's affective rearing behavior directly influenced the EC of boys; boys temperament and cognitive ability, and positive expressions of their teachers indirectly influenced the EC of boys. Girls' temperament and social competence directly influenced the EC of girls; their cognitive ability, mother's affective child rearing behavior, and positive expressions of mothers and fathers indirectly influenced the EC of girls.

• Korean Journal of Child Studies
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• v.21 no.2
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• pp.33-44
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• 2000

In this study, a random sample of 111 six-year-old children were assigned to one of 3 experimental conditions: categorization training using cognitive apprenticeship, categorization training using metacognitive procedures, and no categorization training. Level of intelligence was measured by Raven's(1986) Coloured Progressive Matrics(CPM). No difference was found between apprenticeship and metacognitive training in the high CPM children. In the middle and low CPM levels, cognitive apprenticeship training was more efficient than metacognitive training. This indicates that cognitive apprenticeship, which emphasizes the thinkaloud procedure of both the experts and the novice though modeling and coaching for self-implementation, is a more effective approach than the metacognitive approach for categorization in middle and low CPM children.