• 대한지구과학교육학회지
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• 제5권3호
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• pp.276-286
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• 2012

This research is aimed to examine the differences through comparative analysis of the Earth Science contents in the science textbooks between Korea and DPRK. The contents of level and scope in science textbooks between Korea and DPRK are analysed by TIMSS frameworks. The results of this research are as follows : 1. The science textbooks of DPRK is lower in quality of paper, printing to that of Korea, and the illustrations, editing design in the textbooks of DPRK are fewer, monochromic and monotonous while those in Korea. 2. The contents of Earth Science in DPRK's science textbooks rank 37.0%, but those of Korea's science textbooks rank 25.5% of the whole textbooks. The learning units related to Earth Science are generally similar to the level and scope in science textbooks between Korea and DPRK. The type of inquire activities in the textbooks of DPRK largely takes on the model experiment, and it was shown that the number of experiments directly made by children is very small compared to Korea' textbooks. 3. There are lots of differences in Earth Science learning terms and predicates used in the textbooks between Korea and DPRK.

• 한국콘텐츠학회논문지
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• 제17권9호
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• pp.88-98
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• 2017

본 연구는 대학교육의 책무를 지닌 교원들의 교수역량도구를 개발하고 변화 필요도를 분석한 것이다. 대학 환경 변화에 따른 대학 교원의 책무 수행과 대학내외 공동체 활동에 필요한 역량을 강화하기 위한 교수역량을 도출하였고, 24명의 교수학습 전문가로부터 내용타당도를 검토받아 교수역량도구를 개발하였다. 개발된 도구는 충청남도에 소재한 K대학의 교원 83명을 대상으로 진단을 실시하고, 탐색적 요인분석, 확인적 요인분석을 통해 기초역량, 교육실천역량, 공동체역량을 도출하였다. 기초역량에는 '대학교육의 이해', '교육철학과 자세', '교수자 태도', 교육실천역량에는 '분석설계', '수업실행', '지도관리', '평가환류', 공동체역량에는 '창조적 학문융합', '공감학습문화', '글로벌 공유'로 분류하였다. K대학 교원들의 교수역량을 진단한 후, 대응표본 t-검정과 Borich 계수 분석을 통한 변화필요도를 살펴본 결과 기초역량에서는 대학교육의 이해(4순위), 교육실천역량에서는 분석설계(5순위), 수업실행(2순위), 공동체역량에서는 공감학습문화(1순위), 글로벌 공유(3순위)로 나타났다. 대학 교원의 역량은 잘 가르치기 위한 교육실천의 역량뿐 아니라 대학교육에 대한 이해와 대학내외 공동체 구성원과의 공감과 공유의 역량이 필요하다. 대학에서는 앞으로 교수들의 역량 강화를 위한 지속적인 지원 프로그램을 적극적으로 개발, 제공해야 할 것이다.

• 구강회복응용과학지
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• 제33권2호
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• pp.88-96
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• 2017

목적: 치과 임플란트 캐드 소프트웨어를 이용하여 맞춤형 지대주 디자인 시에 소요되는 시간과 반복학습의 관계를 평가하는 것이다. 연구 재료 및 방법: 맞춤형 지대주 디자인은 3DS 캐드 소프트웨어와 EXO 캐드 소프트웨어를 사용하여 지정된 4개의 단계 순으로 시행되었고, 단계별로 3회 반복 측정하였다. 반복학습에 의한 학습효과는 학습곡선으로 나타냈고, 반복학습에 따른 디자인 시에 소요되는 총 시간과 단계별 소요되는 시간의 감소가 유의한지는 Friedman 검정과 사후검증(Wilcoxon signed rank test)으로 평가하였다. 디자인 시간과 군간의 차이는 반복 측정 이 요인 분석으로 평가하였다. 통계 분석은 SPSS 통계 소프트웨어를 사용하여 수행하였다(P < 0.05). 결과: 맞춤형 지대주 디자인의 반복학습은 횟수와 단계에 따라 유의한 차이를 나타냈다(P < 0.001). 디자인 시간에 따른 차이는 유의한 것으로 나타났으며(P < 0.001), 캐드 소프트웨어 간의 차이도 유의한 것으로 나타났다(P = 0.006). 결론: 캐드 소프트웨어의 반복학습은 디자인 시간을 단축하였고 디자인 평균시간은 3DS 캐드가 EXO 캐드에 비하여 더 적게 소요되었으나, 학습효과에 따른 학습률은 EXO 캐드가 3DS 캐드보다 좋은 결과를 보였다.

• 한국컴퓨터정보학회논문지
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• 제26권7호
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• pp.1-7
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• 2021

본 논문에서는 다중 레이블 분류를 위한 특징 선별 기법을 제안한다. 기존 많은 특징 선별 기법들은 상호정보척도 등을 이용하여 특징과 레이블 사이의 연관성을 계산하여 특징을 선별하였다. 하지만 상호정보척도는 결합 확률을 요구하기 때문에 실제 전제 특징 집합에서 결합 확률을 계산하는 것은 어렵다. 따라서 소수의 특징만 계산이 가능하여 지역적 최적화만 가능하다는 단점을 가진다. 이런 지역적 최적화 문제를 피해, 주어진 특징 전체 공간에서 저랭크 공간을 구성하고, 희소성을 가진 특징들을 선별할 수 있는 특징 선별 기법을 제안한다. 이를 위해 뉴클리어 노름을 이용해 회귀 기반의 목적함수를 설계하였고, 이 목적 함수의 최적화 문제를 풀기 위한 경사하강법 방식의 알고리즘을 제안하였다. 4가지의 데이터와 3가지 다중 레이블 분류 성능을 기준으로 다중 레이블 분류 실험 결과를 통해 제안하는 방법론이 기존 특징 선별 기법보다 좋은 성능을 나타내는 것을 보였다. 또한 제안하는 목적함수의 파라미터 값 변화에도 성능 변화가 둔감한 것을 실험적인 결과로 확인하였다.

• Computers and Concrete
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• 제32권3호
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• 영어어문교육
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• 제9권1호
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• pp.131-152
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• 2003

The purpose of this study is to investigate the correlation between the introduction order of 9 English morphemes in the English textbook used in the middle school and the learning order of the morphemes by the 7th graders learning English as a foreign language. The subjects are 139 students in two middle schools, who learn English with different textbooks. The introduction order of each morpheme in two textbooks was examined according to its quantity and frequency. Data on the real learning order were collected through the written SLOPE test, and each morpheme was ranked by its group score. The introduction order of each morpheme in the textbook and the real learning order were analyzed by Spearman rank order correlation. It was shown that the correlation between the two was very low. This means that those textbooks do not take the learning order of English morphemes into account. Also it was shown that in the earlier stage of learning English the introduction order of each morpheme in the textbook had much influence on its learning order, but in the later stage such influence reduced gradually. This means that the learning order of English morphemes approaches the natural order as time passes by.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• Journal of Information Technology Applications and Management
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• 제27권1호
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• pp.49-58
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• 2020

Lately, a large amount of textual data have been poured out of the Internet and the technology to refine them is needed. Most of these data are long text and often have no title. Therefore, in this paper, we propose a technique to combine the sequence-to-sequence model of RNN and the REINFORCE algorithm to generate the title of the long text automatically. In addition, the TextRank algorithm was applied to extract a summarized text to minimize information loss in order to protect the shortcomings of the sequence-to-sequence model in which an information is lost when long texts are used. Through the experiment, the techniques proposed in this study are shown to be superior to the existing ones.

• 산업경영시스템학회지
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• 제46권2호
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• 대한지구과학교육학회지
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• 제8권1호
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• pp.76-86
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• 2015

The purpose of this study is to take advantage of the smart teaching - learning programs about the unit of 'Earth and Moon' and find out how to apply the effect appears. Teaching-Learning program was conducted over eight lessons. And we analyzed the effect of the program at any time through the evaluation and interview. The results are as follows. First, this study proposed a method to assist in the teaching and learning of spatial ability for students' movement of the Earth and the Moon. The program takes advantage of N-Screen Applications were configured to allow both Earth observation insider perspective and the external multilateral perspective. This improves students' understanding qualitatively. Second, we applied the teaching and learning programs utilizing smart smart devices, and the result was a lot of low rank students' average score rises. In addition, we were able to see that many students' understanding and interest, self-confidence are improved.