• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.141-146
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• 2005

7차 교육과정(1997)의 도입으로 확률 및 통계단원이 중등 수학 교과과정에서 독자적인 영역을 확보하게 된 만큼 그 목적에 부합하면서 효율적이고 흥미를 유발시킬 수 있도록 교과교육 매체 개발에 투자를 해야 할 시기라고 본다. 교과교육 매체의 한 방법으로 Excel을 중학 교육과정에 나타난 통계교과내용을 지원하게끔 Excel의 기본 메뉴, 분석도구, 그리고 Visual Basic을 활용한 매크로 작성방법에 대하여 다루었다.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• Journal of the Korea Computer Industry Society
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• v.9 no.5
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• pp.223-228
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• 2008

A case study to show MS Excel's convenient and powerful functions was conducted to test hypotheses with subway data. Quantitative variables were described using descriptive menu, and qualitative variables were described using histogram menu of a MS Excel software. Relationships were tested using regression menu, differences were tested using t-test menu, and factors were tested using variance-layout menu of a Excel software. Data input, management, and statistical analysis were done successfully with only a MS Excel software.

• 한국정보교육학회:학술대회논문집
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• 2007.01a
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• pp.141-148
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• 2007

ICT는 21세기 지식기반사회에서 교육효과를 증대시킬 수 있는 강력한 도구라는 여러 가지 이유로 ICT 활용교육이 중시되어 왔었고, 학교 현장에서는 ICT를 활용한 교수 학습이 활발하게 도입되었다. 그 중 Excel은 뛰어난 수식 계산과 논리 판단 기능을 갖추고 있어서 간단한 계산에서부터 함수를 이용한 복잡한 수식 작성과 문자의 연산, 데이터의 비교 분석과 그래프를 통한 통계처리까지 거의 모든 종류의 계산을 할 수 있다는 점에서 수학과에서 그 가능성이 제시되어 왔다. 그러나 기존의 연구는 대부분 중 고등학교 수학과에 한정되어 있어 초등학교에서 활용하기에는 무리가 있었다. 이에 본 연구에서는 초등학교 수학과에서의 Excel 활용 수업을 적용하기에 적합한 학습 주제를 선택하여 학생들이 계산하는데 걸리는 시간보다는 문제해결을 위한 사고에 중점을 둔 문제해결 수업모형을 개발하여 적용해 보기로 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.380-380
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• 2022

빗물이용시설은 2014년 「물재이용법」 개정에 따라 시설 확충에 맞춘 획일적인 운영으로 시설수는 증가하고 있으나 대상 시설의 이용량 증가에 대한 부분은 한계를 나타내고 있다. 따라서 설치 의무대상시설에 대한 적정한 시설의 이용현황을 조사 및 분석을 통해 이용시설의 효율적인 운영 및 관리 방안 마련이 필요한 실정이다. 빗물이용시설의 효율적 관리방안 설정을 위해서는 의무 대상시설별 설치 및 운영관리 실태조사, 빗물이용시설관련 기준 검토 및 분석, 빗물이용시설 개선방안 마련의 단계로 방안을 수립하여야 한다. 본 연구에서는 빗물이용시설 기준 검토 및 분석에서 지역별, 수요처별, 용도별 일정 빗물이용 활용률을 확보할 수 있는 빗물이용시설의 평가를 위해 수문학적 물수지 분석을 통한 물수요-공급시계열 분석을 수행하였다. 물수지 분석 툴이 없기 때문에 본 연구를 통해 빗물이용시설 물수지 분석 excel 도구를 개발하였으며, 이를 통해 빗물이용시설 공급충족률(필요수량 대비 빗물이용량), 빗물보장률(전체일수 대비 빗물이용일수), 빗물이용시설 이용률(빗물이용시설용량 대비 빗물이용가능량)에 대해 분석을 수행하였다. 개발한 물수지 분석 excel 도구를 이용하여 현재 설치되고 운영하고 있는 의무대상시설의 운영현황과 도구에서 계산된 활용성 평가 결과의 비교를 통해 현재 운영 현황을 개선할 수 있으며, 다양한 시나리오 적용에 따른 운영 결과를 시설 설치 이전에 확인하여 최적 운영방안 도출에 활용할 수 있을 것으로 판단된다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.905-912
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• 2012

We are dealing with the Bayes' rule education tool with Excel Macro and its usage example. When an event occurs, we are interested in whether it does under certain conditions or not. In this case, we use the Bayes' rule to calculate the probability. Bayes' rule is very useful in making decision based on newly obtained statistical information. We introduce an efficient self-teaching educational tool developed to help the learners understand the Bayes' rule through intermediate steps and descriptions. The concept and examples of intermediate steps such as conditional probability, multiplication rule, law of total probability, prior probability and posterior probability could be acquired through step-by-step learning. All the processes leading to result are given with diagrams and detailed descriptions. By just clicking the execution button, users could get the results in one screen.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.403-410
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• 2014

Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.

• Journal of Educational Research in Mathematics
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• v.15 no.1
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• pp.75-105
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• 2005

This study explored a mathematical modeling flow and the effect of interactions among students and between a student and Excel on modeling in a small group modeling environment with Excel. This is a case study of three 8th graders' modeling activity using Excel during their extra lessons. The conclusions drawn from this study are as follows: First, small group modeling using Excel was formed by formulating 4∼10 modeling cycles in each task. Students mainly formed tables and graphs and refined and simplified these models. Second, students mainly formed tables, algebraic formulas and graphs and refined tables considering each variable in detail by obtaining new data with inserting rows. In tables, students mainly explored many expected cases by changing the values of the parameters. In Graphs, students mainly identified a solution or confirmed the solution founded in a table. Meanwhile, students sometimes constructed graphs without a purpose and explored the problem situations by graphs mainly as related with searching a solution, identifying solutions that are found in the tables. Thus, the teacher's intervention is needed to help students use diverse representations properly in problem situations and explore floatingly and interactively using multi-representations that are connected numerically, symbolically and graphically. Sometimes students also perform unnecessary activities in producing data by dragging, searching a solution by 'trial and error' and exploring 'what if' modeling. It is considered that these unnecessary activities were caused by over-reliance on the Excel environment. Thus, the teacher's intervention is needed to complement the Excel environment and the paper-and-pencil environment properly.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.4
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• pp.265-280
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• 2009

This study investigated whether information literacy education contributes to change and improvement in university students' self awareness of information literacy and libraries. The pre-test and the post-test which comprised the same questions on information literacy and libraries were executed on 47 students in an information literacy-related regular course at D University. The information literacy-related questions in the questionnaire were based on ACRL Information Literacy Competency Standards for Higher Education. The collected data were analyzed using Excel and SPSS.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.247-258
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• 2015

Students are engaged in classroom learning, and classroom learning occurs not only through conversation but also through nonverbal behavior. In science classrooms especially, there are meaningful nonverbal behaviors such as practical activities like observation and measurement. But these behaviors have not been properly investigated by existing instruments that try to measure students' engagement. This study aims to develop a new instrument for analyzing students' behavioral engagement especially in science classrooms. The method of developing the instrument was structured along three steps. First, student behaviors have been classified into fourteen categories through literature review and a series of observation of elementary science classroom. Second, based on these, a framework for analyzing student behavioral engagement has been developed. With the framework, every student moment could be labeled as Participatory Speech or Participatory Silence or Non-Participatory Speech or Non-Participatory Silence. Third, an instrument to which the framework is applied has been developed by using Microsoft Excel. As a trial, two fourth-grade students in elementary science class were analyzed with this instrument. The results of the trial analysis shows that the longest period of a science lesson was occupied by Participatory Silence (63% and 72%). Among the participatory silence, 'listening' was the most common (51% and 42% of the trial lesson) and 'observing' which is a specific behavior to science was the fourth position (17% and 17% of the trial lesson). It is expected that the developed instrument could be used in improving our understanding of the patterns of student engagement in science classrooms.