• Educational Technology International
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• v.22 no.2
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• pp.139-167
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• 2021

After experiencing school closures and online learning caused by COVID-19, the important role of school education was reinforced. Elementary school is the foundation of life and allows students to develop both social and academic skills. The purpose of this study was to examine the mediational role of Self-Regulatory Skills (SRS) on the relationship between Maternal Parenting Attitude (MPA) and School Adjustment (SA) of elementary school students. A total of 99 students enrolled in an international school in Seoul, Korea from grades 3 to 6 participated in this study. Data were analyzed through Independent Sample T-Test, one way ANOVA, Multiple Regression Analysis, and Hierarchical Regression Analysis using the SPSS 23.0. The findings of the study were as follows. First, there is a difference between genders and among grades. Second, only acceptance was significantly related to school adjustment. Third, acceptance, strict control, and accepted control are significantly related to SRS. Fourth, Self-Regulatory Skills (Sustained Attention) fully mediate the relationship between Maternal Parenting Attitude (Acceptance) and School Adjustment (Academic Attitude/ Rule compliance). Educational implications for understanding the role of parenting attitude and future directions are discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.53-64
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• 2021

Depression is one of the most important psychiatric disorders worldwide. Most depression-related data mining and machine learning studies have been conducted to predict the presence of depression or to derive individual risk factors. However, since depression is caused by a combination of various factors, it is necessary to identify the complex relationship between the factors in order to establish effective anti-depression and management measures. In this study, we propose a methodology for identifying and interpreting patterns of depression expressions using the method of deriving random forest rules, where the random forest rule consists of the condition for the manifestation of the depressive pattern and the prediction result of depression when the condition is met. The analysis was carried out by subdividing into 4 groups in consideration of the different depressive patterns according to gender and age. Depression rules derived by the proposed methodology were validated by comparing them with the results of previous studies. Also, through the AUC comparison test, the depression diagnosis performance of the derived rules was evaluated, and it was not different from the performance of the existing PHQ-9 summing method. The significance of this study can be found in that it enabled the interpretation of the complex relationship between depressive factors beyond the existing studies that focused on prediction and deduction of major factors.

• Electronics and Telecommunications Trends
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• v.36 no.3
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.289-294
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• 2022

의존구문분석은 문장을 의존관계(의존소-지배소)로 분석하는 구문분석 방법론이다. 현재 사전학습모델을 사용한 전이 학습의 딥러닝이 좋은 성능을 보이며 많이 연구되지만, 데이터셋에 의존적이며 그로 인한 자료부족 문제와 과적합의 문제가 발생한다는 단점이 있다. 본 논문에서는 언어학적 지식에 기반한 강화된 지배소-의존소 제약규칙 에지 알고리즘을 심층학습과 결합한 모델을 제안한다. TTAS 표준 가이드라인 기반 모두의 말뭉치로 평가한 결과, 최대 UAS 96.28, LAS 93.19의 성능을 보였으며, 선행연구 대비 UAS 2.21%, LAS 1.84%의 향상된 결과를 보였다. 또한 적은 데이터셋으로 학습했음에도 8배 많은 데이터셋 학습모델 대비 UAS 0.95%의 향상과 11배 빠른 학습 시간을 보였다. 이를 통해 심층학습과 언어지식의 결합이 딥러닝의 문제점을 해결할 수 있음을 확인하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.1-9
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• 2022

The PoN (Proof of Nonce) distributed consensus algorithm basically uses a non-competitive consensus method that can guarantee an equal opportunity for all nodes to participate in the block generation process, and this method was expected to resolve the first trilemma of the blockchain, called the decentralization problem. However, the decentralization performance of the PoN distributed consensus algorithm can be greatly affected by the network transaction transmission delay characteristics of the nodes composing the block chain system. In particular, in the consensus process, differences in network node performance may significantly affect the composition of the congress and committee on a first-come, first-served basis. Therefore, in this paper, we presented a problem by analyzing the decentralization performance of the PoN distributed consensus algorithm, and suggested a fairness control algorithm using a learning-based probabilistic acceptance rule to improve it. In addition, we verified the superiority of the proposed algorithm by conducting a numerical experiment, while considering the block chain systems composed of various heterogeneous characteristic systems with different network transmission delay.

• Korean Journal of Construction Engineering and Management
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• v.24 no.4
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• pp.60-73
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• 2023

The need for adopting automation technologies to improve inefficiencies in interior finishing modeling work is increasing during the Building Information Modeling (BIM) design stage. As a result, the use of visual programming languages (VPL) for practical applications is growing. However, undefined or incorrect space designations in BIM models can hinder the development of automated finishing modeling processes, resulting in erroneous corrections and rework. To address this challenge, this study first developed a rule-based automated interior finishing detailing module for floors, walls, and ceilings. In addition, an automated space integrity checking module with 86.69% ACC using the Multi-Layer Perceptron (MLP) model was developed. These modules were integrated into a design automation module for interior finishing, which was then verified for practical utility. The results showed that the automation module reduced the time required for modeling and integrity checking by 97.6% compared to manual work, confirming its utility in assisting BIM model development for interior finishing works.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.385-398
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• 2020

This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.170-182
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• 2012

The purpose of this study was to develop learning materials based on the Kepler's abductive reasoning and to identify high school students' rule-inferring strategies on the law of planetary motion. The learning materials including the concepts of solar magnetic field, conservation of figure skater's angular momentum and Kepler's polyhedral theory were developed and the questions about Kepler's 2nd and 3rd law of planetary motion were also created. The participants were 79science high school students and 83general high school students. The patterns and properties of their abductive inference were analyzed. The findings revealed that the students showed 'incomplete analogy abduction', 'analogy abduction' and 'reconstruction' to generate the hypotheses concerning the Mars' motion related to the solar magnetic field. There were more general high school students who showed the incomplete analogy abduction than science high school students. On the other hand, there were more science high school students who showed the analogy abduction and reconstruction strategy than general high school students. Also, they showed 'incomplete analogy abduction', 'analogy abduction' and 'model construction and manipulation' to generate the hypotheses concerning Kepler's second law. A number of general high school students showed the incomplete analogy. It is suggested that because the analogy of figure skater cause the students' alternative framework to use, more detailed demonstration is necessary in class. In addition, students combined Kepler's polyhedral theory with their prior knowledge to infer Kepler's third law.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.428-436
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• 2020

The purpose of this study is to analyze the needs for developing a curriculum for strengthening the long-term care service expertise and job competency. Specifically, the researchers analyzed previous studies on national long-term care services and national policy data, and conducted focus group interviews with 14 experts from related agencies. Activity theory was applied as a framework for analysis and a questionnaire about the importance and difficulty of subjects from 25 long-term service employees was administered for validating the results of the qualitative data analysis. The upper part of the subject-goal-tool of the activity system was considered the main area of action, and the following rule-community-division was divided into contextual parts for action, and the implications for demand analysis and future operation of the online curriculum are summarized. In total, six courses were required for development. These courses could be applied to as a learner-centered flip learning for long-term care service workers and various educational methods of collective education and supplementary education have been proposed. Based on the study results, implications in the educational field for effective management of courses were suggested at the end of the study.

• The Journal of Korean Philosophical History
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• no.32
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• pp.161-183
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• 2011

Lee Ik(李瀷) had put emphasis on the achievements by self-regulated academic learning through doubts, and at the same time that it was all-embracing. His academic attitude had set an example among the members of Seongho school(星湖學派), and his disciples had strived to emulate his style. The greatness of Seongho(星湖)'s study had been revealed by development of Seongho school(星湖學派) right after his death. He had argued that the six strokes of I-Ching should be read having it divided into inward and outward divine signs. He had stated his view clearly that the divine signs ranging from one stroke to six strokes were not connected, same as Shao yong(邵雍)'s method, but, the three strokes of inward divine sign as well as the three strokes of outward divine signs were independent from each other. Seongho(星湖) also had raised many questions about Shifa(筮法), and Bianyao(變爻) and Zhuzi(朱子)'s Shifa(筮法), or Yixueqimeng("易學啓蒙") "Kaobianzhan("考變占")". In view of the Shifa(筮法), Seongho(星湖) had helped Dasan(茶山) to present 'Shiguafa(蓍卦法)' by proposing different divination rule from Zhuzi(朱子)'s Method of Divination by Shiyi("筮儀"). Seongho(星湖) had not professed something significantly different from Zhuzi(朱子) in his I-xue. His study on I-xue had been accomplished under his goal of achievements by self-regulated academic learning through doubts. "Shiguakao("蓍卦攷")" is also same. I-xue of Seongho(星湖) had made a great contribution to form Dasan(茶山)'s I-xue in the later years.