• 대한안전경영과학회지
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• 제19권3호
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• pp.97-107
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• 2017

This research studies on the demand forecasting for service parts considering parts life cycle, that gets relatively less attentions in the field of forecasting. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods, then we propose the new demand forecasting method by using these findings and reinforcement leaning technique. Using simulation experiments, we proved that the proposed forecasting method is better than the existing methods under various experimental environments.

• 한국보육지원학회지
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• 제10권6호
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• pp.223-241
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• 2014

본 연구의 목적은 미국의 0~만 2세 영아와 만 3~4/5세 유아의 조기학습기준의 분석으로 살펴본 교육적 시사점을 통하여 한국의 자연탐구영역의 영아와 유아교육과정의 연계 방향을 모색하는 것이다. 자료수집은 미국의 각 주별 조기학습기준 중 0~만 2세와 만 3~4/5세로 연령이 분리된 12개주와 연령구분을 별도로 하지 않고 0~만 4/5세로 연령연계의 틀로 구성된 주 5개주이다. 연구문제에 따른 분석결과는 다음과 같다. 첫째, 0~만 4/5세로 연령이 연계된 5개 주의 조기학습기준의 자연탐구영역과 관련된 내용 영역은 모든 주에서 수학과 과학에 포함되고 인지발달과 추론에 일부 포함되었다. 그러나 0~만 2세와 만 3-4/5세로 연령이 분리된 12개 주의 경우는 0~만 2세는 인지발달이 포함되고 있지만 만 3~4/5세는 수학과 과학이 포함되어 있었다. 둘째, 0~만 2세의 10개 주의 조기학습기준의 자연탐구영역과 관련된 인지발달의 12개 하위 내용 중 개념발달과 기억, 문제해결, 탐색과 발견의 순으로 많이 포함되어 있었다. 셋째, 0~만 4/5세로 연령이 연계된 5개 주의 조기학습기준의 자연탐구영역과 관련된 내용 영역은 과학적 사고, 생물, 물리, 지구와 우주가 많이 포함되어 있었다. 그러나 연령이 분리된 12개주의 자연탐구영역의 관련 내용 영역은 0~만 2세는 2개주에서 생물, 물리, 지구와 우주가 모두 포함되어 있었다. 넷째, 연령이 연계된 주의 수학 영역의 내용은 수와 연산, 측정, 기하와 공간이 많이 포함되어 있었다. 연령이 분리된 주의 경우는 0~만 2세는 수와 연산, 기하와 공간이 모두 포함되어 있었고 만 3~4/5세는 수와 연산, 기하와 공간, 측정이 모두 포함되어 있었다. 본 연구를 통해 교육과정 개발 시 영아와 유아를 대상으로 발달 측면과 교과 측면의 연계성에 대한 검토가 필요함을 알 수 있었다.

• 한국과학교육학회지
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• 제18권3호
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• pp.451-458
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• 1998

본 연구에서는 초 중등학교의 전 교과에서 다루어진 식물 학습 소재의 다양성을 조사 분석하여 과학 교과서(생물영역)의 식물 학습 소재의 개발 방향성을 모색하고자 하였다. 분석에 사용된 교과서는 초등학교 9교과 143권, 중학교 13교과 117권, 고등학교 15교과 71권 등 총 331권이다. 본 연구의 결과는 다음과 같다. 1. 초 중등학교 전 교과의 학교 급간별 식물의 종 수는 초등학교 264종, 중학교 295종, 고등학교 283종으로 과학교과서(생물영역)의 103종, 206종, 193종보다 많다. 타 교과에 식물의 종 수가 많다는 것은 식물 학습 내용도 많이 포함하고 있다는 것을 의미한다. 2. 초 중등학교에서 식물 학습 소재가 많이 나타난 타 교과는 국어, 사회, 실과(실업 가정)로 단순한 식물 인용뿐만 아니라 과학 개념 및 탐구 활동을 내포하고 있다. 식물 학습 소재는 과학 교육 과정뿐 아니라 학교 교육 과정에서 큰 비중을 차지하고 있다. 3. 타 교과에서 인용되는 식물 학습 소재의 특징은 주변 식물, 자원 식물, 식용 식물, 관상 식물, 산업에 관련된 식물 등 유용성 있는 생활 관련 식물이다. 4. 과학과 생활과의 관계를 인식하여 타 교과의 식물학습 소재를 과학 교과(생물영역)의 식물 소재 개발에 적용한다면 STS 교육 및 통합 교과의 근거를 제공해 줄 수 있다고 본다.

• 한국과학교육학회지
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• 제27권8호
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• pp.711-723
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• 2007

As the discourse of scientific literacy has broadly summed up the goals of science education in the current decade, this study attempts to question how we contextualize appropriate interpretations and feasible approaches to scientific literacy in K-6 science education. With respect to the complex praxis of scientific knowledge and practice, this study emphasizes the participatory framework of scientific literacy which interweaves children's everyday experiences and science learning. This study also concerns children's abilities to understand and enact scientific enterprises (i.e., children's fundamental sense of scientific literacy). As a way of developing K-6 scientific literacy, this study investigates how using science books can broaden the scope of children's understandings of science in life connections and promote a fundamental sense of scientific literacy through talking, reading, and writing skills in Grade two science classrooms in Canada. Second graders were engaged in learning "sound" for five weeks. During science lessons, children's talks were recorded and their writings were collected for data interpretation. This research finds that using science books can encourage children to become engaged in communicative activities such as talking, reading, and writing in science; furthermore, using science books develops children's inquiry skills. These findings open a further discussion on scientific literacy at the K-6 levels.

• 한국초등과학교육학회지:초등과학교육
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• 제35권1호
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• pp.1-12
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• 2016

The purpose of this study was to analyze question's patterns in elementary school science teacher's guide books of 5, 6th grade under the 2009 revised curriculum. A modified analysis framework based on Blosser's classified system was used to analyze 1,982 questions extracted from elementary science teacher's guide books by grade, by domain, and by teaching and learning stage. The findings of this study were as follows. First, of the 1,982 questions, the most prominent type of question was the propositional question and the following was the reproductive question. And, in comparing the question's patterns between 5, 6th grade, it was found that 6th grade had higher rate of close typed question, while 5th grade had higher rate of open typed question in its curriculum. Secondly, a comparative study about two domains, material and energy science domain and earth and life science domain, showed that the number of questions of each domain was not much different. However, it was found that propositional questions and applicable questions showed a higher rate in material and energy science domain, and anticipated questions and open typed questions including divergent and evaluative question showed higher rate in earth and life science domain. Moreover, although the total number of questions from integration and my fun research domain's contents was small, the rate of open typed questions was higher than any other domains. Finally, as a result of comparing and analyzing question's pattern in teaching and learning stages, the rate of reproductive question and anticipated questions was high at the stage of introduction. At the stage of development, the rate of propositional and reproductive questions was high. At the stage of conclusion, the rate of synthetic and applicable questions was high.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• 한국비블리아학회지
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• 제22권3호
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• pp.5-24
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• 2011

각 교과의 교사들이 효과적으로 수업을 전개하기 위해서는 사전에 수업 전개 과정을 설계할 필요가 있다. 전통적으로 교수설계에 널리 적용되고 있는 모형으로는 딕과 케리, ADDIE, ASSURE 등이 있다. 그러나 전통적인 교수설계 모형은 교과 내용이 소규모 단원으로 분절되어 있어서 학생들이 교과의 본질적인 이해에 이르기 어렵다는 한계가 있다. 이러한 문제를 해결하기 위해서 위깅스와 멕타이는 백워드 설계 모형을 제안하였다. 백워드 설계 모형은 가르쳐야 할 내용과 활동을 결정하기 전에 먼저 평가를 고려하는 교수설계 방법이다. 이에 본 연구에서는 백워드 모형의 구조를 분석하고, 정보활용교육을 위한 '도서관과 정보생활' 교과에 백워드 설계 모형을 적용함으로써 정보활용교육의 효과적인 교수-학습 방법을 모색하고자한다. 본 연구에서는 '도서관 과 정보생활' 교과의 백워드 교수설계를 위해서 위깅스와 메타이가 고안한 템플릿을 적용하였다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.269-279
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• 2021

In the current conditions of world socio-economic development, the strategic support of the process of managing the development of universities has become a particularly important area. Strategic management requires reliable information and analytical support in the form of sound descriptions of strategic directions of development, assumptions, and forecasts. The purpose of the study is to substantiate and elaborate the crucial causes in the strategic management of university's development and to suggest the coherent prospects for advancements. The data analysis was performed using descriptive methods to identify the most significant causes that affect the university's strategic development; the expert assessment was used to rank the factors, ultimately to assess each factor that affects to some extent the university's strategic development; the abstract-logical method was used to ground the positive impact of computer technologies and e-learning on the strategic development of a university and to formulate proposals for its further progress. The main results provided in the given paper showed that significant and most important strategic cause of university's development lies in the field of improving the quality of education, expanding access to educational services based on computer technology and its functionality. In turn, its widespread use at all stages of the educational process allows providing a number of advancements for universities in strategic prospects.