• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.25-30
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• 2021

2022 개정 교육과정에서 AI교육의 도입에 따라 데이터 관련 교육의 필요성이 대두되고 있는 상황에서 학생들의 데이터 기반 문제해결력을 향상 시키는 것이 필요하다. 본 연구는 이러한 필요성에 따라 학생들의 데이터 기반 문제해결력을 향상하기 위한 SW교육에 대한 방안을 연구하고자 한다. ADDIE 교수설계모형을 바탕으로 프로그램을 설계하였고, 교사를 대상으로 요구분석 설문조사를 실시하여 요구 분석을 실시하였다. 요구분석 결과에 근거하여 마이크로비트를 활용한 데이터 기반 문제해결을 주제로 교육 프로그램을 설계하였다. 본 연구에서는 데이터 기반 문제해결의 중요성과 그 역량의 필요성을 제기하였다. 후속 연구에서는 데이터 기반 문제해결 SW 교육이 문제해결력에 어떤 유의미한 효과성을 나타낼지 밝히는 것이 필요하다.

• 정보교육학회논문지
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• 제25권5호
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• pp.713-721
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• 2021

교육부에서 2022 개정 교육과정에 본격적으로 AI교육을 도입하면서 AI교육과 더불어 데이터 관련 교육의 필요성에 대한 공감도가 높아지고 있다. 인공지능을 제대로 이해하고 활용하는 역량을 기르기 위해서는 데이터에 대한 이해와 활용 역량이 기반되어야 한다. 본 연구에서는 요구분석, 선행연구분석 결과를 종합하여 마이크로비트를 활용한 데이터 기반 문제해결 SW교육 프로그램을 개발하였다. 데이터 기반 문제해결 교육 프로그램은 데이터 과학의 내용 중 초등학생을 대상으로 적용할 수 있는 교육 요소들로 구성하여 개발되었다. 본 연구에서 개발한 프로그램을 통해 실생활 데이터를 바탕으로 다양한 주제와 교과를 융합한 교육을 연계할 수 있다. 더 나아가 데이터에 대한 이해를 바탕으로 보다 내실 있는 AI교육 프로그램의 기반을 갖추게 될 것이다.

• 한국수학교육학회지시리즈A:수학교육
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• 제63권2호
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• pp.233-254
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• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• 한국시뮬레이션학회논문지
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• 제5권1호
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• 한국빅데이터학회지
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• 제8권1호
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• 디지털융복합연구
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• 제13권1호
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• pp.227-235
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• 2015

빅데이터분석은 조직의 문제해결을 위한 융합적 수단이다. 효과적인 문제해결을 위해서는 문제의 형태, 데이터의 유형 및 존재여부, 데이터 분석역량, 분석을 위한 기반정보기술의 수준 등 다양한 요인을 융합적으로 고려하여 문제해결의 접근법이 결정되어야 한다. 본 연구에서는 기획 접근법으로 논리적인 하향식 접근법, 데이터기반의 상향식 접근법, 그리고 문제해결 환경의 불확실성을 극복하기 위한 프로토타이핑 접근법 등 세 가지 유형을 제안한다. 특히, 이 유형 중에서 창의적 문제해결과 상향식 접근법이 어떤 연관성을 갖는지 살펴본다. 또한 데이터 거버넌스와 데이터 분석역량을 융합적으로 고려하여 조직의 빅데이터분석의 소싱과 관련한 주요 전략적 이슈를 도출한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• 한국경영과학회지
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• 제29권4호
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 1998년도 춘계 학술대회논문집
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• pp.15-28
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• 1998

As an alternative for solving the incompressible Navier-Stokes equations, we present a vorticity-based integro-differential formulation for vorticity, velocity and pressure variables. One of the most difficult problems encountered in the vorticity-based methods is the introduction of the proper value-value of vorticity or vorticity flux at the solid surface. A practical computational technique toward solving this problem is presented in connection with the coupling between the vorticity and the pressure boundary conditions. Numerical schemes based on an iterative procedure are employed to solve the governing equations with the boundary conditions for the three variables. A finite volume method is implemented to integrate the vorticity transport equation with the dynamic vorticity boundary condition . The velocity field is obtained by using the Biot-Savart integral derived from the mathematical vector identity. Green's scalar identity is used to solve the total pressure in an integral approach similar to the surface panel methods which have been well-established for potential flow analysis. The calculated results with the present mettled for two test problems are compared with data from the literature in order for its validation. The first test problem is one for the two-dimensional square cavity flow driven by shear on the top lid. Two cases are considered here: (i) one driven both by the specified non-uniform shear on the top lid and by the specified body forces acting through the cavity region, for which we find the exact solution, and (ii) one of the classical type (i.e., driven only by uniform shear). Secondly, the present mettled is applied to deal with the early development of the flow around an impulsively started circular cylinder.

• 한국정보시스템학회지:정보시스템연구
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• 제20권3호
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.