• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.209-210
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• 2017

본 연구에서는 초등에서 알고리즘 교육의 방법으로 사용되는 언플러그드 활동에 순서도를 활용하는 방안을 제안한다. 소프트웨어 교육의 기본적인 방향은 컴퓨팅 사고력을 기반으로 문제를 해결하는 역량을 기르고자 하는 것[1]이지만, 현재는 교육용 프로그래밍 언어의 학습에 치우쳐 있거나 알고리즘 교육이 제대로 이루어지고 있지 않다. 언플러그드 활동에 순서도를 활용함으로써 알고리즘 영역의 교육을 강화하고 균형 잡힌 컴퓨팅 사고력 함양에 도움이 될 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.1-2
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• 2022

본 논문은 4차 산업혁명의 핵심기술인 인공지능과 IoT를 피지컬 컴퓨팅을 이용해 교육을 할 수 있는 플랫폼을 설계하였다. 플랫폼은 파이썬 비주얼 블록 프로그래밍을 기반으로 사용자의 코딩 언어의 구문적인 어려움을 감소시키며 데이터 분석과 머신러닝을 쉽게 응용할 수 있다. 피지컬 컴퓨팅을 위한 AIoT 타겟 보드로는 라즈베리파이를 활용하였으며 타겟보드의 하드웨어에 대한 선수 지식을 최소화해서 원하는 시스템을 개발할 수 있다. 응용에서는 센서로 수집한 데이터를 분석하고 인공지능 모델 생성을 할 수 있으며 학습된 모델을 액추에이터 제어에 활용하는 등 AIoT 피지컬 컴퓨팅 교육에 여러 장벽을 낮추었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.967-972
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• 2020

Most of the experimental tools currently used are applied to experiments in the physical field by utilizing sensors and only MBL that are suitable for specific experiments have been developed. However, There is no experimental design stage using SW fusion, and there is a limit to the application of various chemistry experiments in textbooks, and in the case of Arduino, it is difficult for students to learn and understand language when programming. In this paper, we designed and developed a SW education convergence science experiment apparatus including a learner's active experiment design process, overcoming the shortcomings of the existing microcomputer experiment and the limitations of software education.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.85-90
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• 2023

Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11B
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• pp.1363-1372
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• 2011

Flooding based routing protocols are usually used to disseminate information in wireless sensor networks. Those approaches, however, require message retransmissions to all nodes and induce huge collision rate and high energy consumption. In this paper, HoGoP (Hop based Gossiping Protocol) in which all nodes consider the number of hops from sink node to them, and decide own gossiping probabilities, is introduced. A node can decide its gossiping probability according to the required average reception percentage and the number of parent nodes which is counted with the difference between its hop and neighbors' ones. Therefore the decision of gossiping probability for network topology is adaptive and this approach achieves higher message reception percentage with low message retransmission than the flooding scheme. Through simulation, we compare the proposed protocol with some previous ones and evaluate its performance in terms of average reception percentage, average forwarding percentage, and forwarding efficiency. In addition, average reception percentage is analyzed according to the application requirement.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.51-56
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• 2021

This paper studies VPBL(Various PBL) models, applies them to classes, and analyzes results so that students of various majors can design and implement problems according to the characteristics of their majors in order to improve problem solving in education. VPBL performs the process of designing and implementing problems that reflect the characteristics of the major by applying constraints to the professor's programming language. The professor performs mini_class in the process of solving the designed problem and then shares it throughout. VPBL model apply results, The traditional teaching method was 3.51 points and the application of the VPBL model was 4.52 points, and "interaction, understanding of learning contents, and acquiring knowledge related to curriculum" were improved. In addition, VPBL has the advantage of expanding the learning range in the solving process as it is based on various problem solving, which has the effect of expanding the learning range compared to existing class models. Research on the expanded application of various SW liberal education in the future is required.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1077-1094
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• 2016

Apache Spark is a fast and general-purpose cluster computing package. It provides a new abstraction named resilient distributed dataset, which is capable of support for fault tolerance while keeping data in memory. This type of abstraction results in a significant speedup compared to legacy large-scale data framework, MapReduce. In particular, Spark framework is suitable for iterative machine learning applications such as logistic regression and K-means clustering, and interactive data querying. Spark also supports high level libraries for various applications such as machine learning, streaming data processing, database querying and graph data mining thanks to its versatility. In this work, we introduce the concept and programming model of Spark as well as show some implementations of simple statistical computing applications. We also review the machine learning package MLlib, and the R language interface SparkR.

• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.53-60
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• 2018

In this study we have developed a Python education program that can be applied to students who have studied block-based coding. We have developed a Python education program based on the extracted the learners' level of block-based coding by analyzing the programs and the textbooks. We extracted the grammar of the block-based coding and constructed the curriculum. Then, the Python education program was composed by 16 hours. After reviewing the appropriateness of the education program through expert validation, it was concluded that the developed Python education program is suitable for applying to learners of block-based coding. We expect that proposed program will be effectively applied as basic resources to learn script coding in class.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.415-425
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• 2019

The purpose of this study is to analyse of elementary student experience in software education based on computational thinking framework. A total of 27 students (5th grade) were interviewed who took software education during 4 months in A elementary school which located in Seoul. The findings revealed were as follows: First, the elementary learners were able to perform scratch projects and learn basic concepts for computing thinking. elementary students' studied basic concepts for computational thinking by the process of carrying out their Scratch project. Second, elementary learners were able to confirm the execution of computing accidents in the process of implementing scratch projects. Third, elementary students had change in creative expression and interactive connectivity in terms of learners' computing thinking. The result of this study is meaningful in that it analyzes the educational experience of elementary school students qualitatively and suggests the direction of software education for enhancing computing thinking ability.

• Journal of Creative Information Culture
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• v.5 no.1
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• pp.35-43
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• 2019

In this study, we analyzed how information and technology education in elementary and middle school curriculum affects learning achievement of computational thinking courses in university. We conducted a questionnaire survey on students who took computer-related courses for the past year and collected data on what type of computer-related education they received. As a result of analyzing the data, students who received computer-related education in the previous curriculum showed higher overall academic achievement. However, there was a significant difference in learning achievement according to types of contents, and it is necessary to consider the design and improvement of efficient computational thinking education in the future through the results. It is also necessary to continue the analysis of the impact of the new education curriculum.