• 한국문헌정보학회지
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• 제47권2호
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• pp.223-245
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• 2013

본 연구는 국내외 대학 문헌정보학 주요 교과목의 강의계획서 내의 수업내용, 수업방법 및 평가방법을 비교 분석하여 그 특징과 차이점을 파악하는 것을 목적으로 한다. 연구 결과는 국내 문헌정보학 교육내용 및 방법 개선에 기여할 수 있을 것이다. 이를 위해 국외 문헌정보학과의 정보조직론, 정보서비스론, 정보검색론, 도서관경영론의 강의계획서를 수집하여 수업내용과 방법 및 평가방법을 분석하였다. 그 결과, 각 과목별로 교과내용과 수업방법, 평가방법에 있어서 국외대학과 국내대학 간에 차이점이 있는 것으로 나타났으며, 특히 수업방식에서 국내대학에서는 강의, 발표, 시험이 공통적으로 사용되는 것과는 달리 외국의 경우 워크숍, 프로젝트, 시스템모델개발, 시뮬레이션, 사례연구, 전문가인터뷰 등 다양하게 시행되는 것으로 분석되었다.

• 분석과학
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• 제27권1호
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• pp.41-59
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• 2014

국내 시판제품으로 서울시내에서 구입한 산업용 윤활유, 이륜구동 윤활유, 선박용 윤활유, 자동차용 윤활유(엔진오일, 수동 변속기 기어유, 자동변속기 오일) 등 80종(기유 4종 포함)의 새 윤활유들(80 classes)과 8종의 경유 차량과 16종의 휘발유 차량에 각각 3종씩의 경유와 휘발유 전용 엔진 오일로 교환하여 차량별 및 주행거리별로 각각 채취한 사용 엔진 오일 86종을 GC/MS로 분석한 TIC로 데이터베이스를 만들고, 새 윤활유와 사용 엔진오일들의 동일성 추적과 차량별 분류를 위하여 차원 축소와 베이지안 방식의 분류 모형을 개발하였다. 새 윤활유의 분류는 웨이블렛 적합방법과 주성분 분석방법으로 차원 축소하여 베이지안 방식의 분류 모형을 적용한 결과 각각 97.5%와 96.7%의 정분류율을 보여 차원 축소는 웨이블렛 적합방법이 더 좋은 결과를 나타냈다. 그리고 새 윤활유의 분류에서 선택된 웨이블렛 적합방법의 차원 축소와 베이지안 방식의 분류 모형에 의한 사용 엔진 오일의 차량별 분류(총 24 classes)는 86.4%의 정분류율을 보였고, 경유 차량인지 휘발유 차량인지를 구분하는 차량 연료 타입별 분류(총 2 classes)는 99.6%의 정분류율을 나타내었고, 사용 엔진 오일 브랜드별 분류(총 6 classes)는 97.3%의 정분류율을 나타내었다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.145-152
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• 2022

One of the impacts of Covid-19 on education systems has been the shift to online education. This shift has changed the way education is consumed and perceived by students. However, the exact nature of student perception about online education is not known. The aim of this study was to understand the perceptions of Saudi higher education students (e.g., post-school students) about online education during the Covid-19 pandemic. Various aspects of online education including benefits, features and cybersecurity were explored. The data collected were analysed using statistical techniques, especially artificial neural networks, to address the research aims. The key findings were that benefits of online education was perceived by students with positive experience or when ensured of safe use of online platforms without the fear cyber security breaches for which recruitment of a cyber security officer was an important predictor. The issue of whether perception of online education as a necessity only for Covid situation or a lasting option beyond the pandemic is a topic for future research.

• 농업과학연구
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• 제35권1호
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• pp.53-67
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• 2008

The principle goal of environmentally-friendly agriculture is to maintain and conserve water and agriculture environment including drinking water resources by properly using agricultural materials such as agricultural chemicals, chemical fertilizers, and other agricultural materials according to act 19 of foster law of environmentally-friendly agriculture. To achieve these goals, we have to establish Integrated Nutrient Management(INM) and Integrated Pesticide Management(IPM) which are most important core technologies for environmentally-friendly rice cultivation. However, there are lack of criteria and technology for evaluation category according to soil management and its soil classes to practice an environmentally-friendly agriculture. Therefore, we should eatablish the standards to produce the safe agricultural products based on the soil physical and chemical characteristics which are basic properties of soil to accomplish the principle aims of environmentally-friendly agriculture.

• 공학교육연구
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• 제24권6호
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• pp.79-85
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• 2021

In a future society where cutting-edge science technology such as artificial intelligence becomes commonplace, the demand for talented people with basic knowledge of mathematics and science is expected to increase continuously, and the educational infrastructure suitable for the characteristics of future generations is still insufficient. In particular, in the case of students taking convergence courses including practical training, there was a problem in communication with the instructor. In this study, we looked at the current status of distance learning at domestic universities that came suddenly due to the global pandemic of COVID-19. In addition, a case study of the use of technology was conducted to facilitate the interaction between instructors and learners through case analysis of distance classes in convergence subjects. Therefore, this study aims to introduce the case of developing lecture contents for smooth convergence education in a non-face-to-face educational environment targeting the developed AI convergence courses and applying them to the education of enrolled students.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.159-168
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• 2022

No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.

• 한국과학교육학회지
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• 제28권7호
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• pp.730-748
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• 2008

Students often have tenacious physics misconceptions and many studies were conducted on engendering conceptual change. Correspondingly, there is much literature on alternative assessment and its role in student learning. This is a comparison study on using alternative assessments to improve common students' misconceptions in GCE Advanced Level Physics. This research also aims to affirm alternative assessment as a valid tool for learning and promote its use. This study involved two classes with 24 students each. For four weeks, electromagnetism was taught to students using the same classroom pedagogies but with different assignments. The control group completeda standard drill-and-practice assignment while the experimental group finished an alternative assessment. From the preliminary results, students who undertook the alternative assessment and the traditional assessment both improved, however, the treatment group did not perform statistically significantly better than the control group. The reasons will be discussed and commented and it is expected to have significant improvement on rectifying misconceptionsupon next batch of experimentation groups.

• 대한화학회지
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• 제65권2호
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• pp.133-144
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• 2021

초중등 과학교사들은 인성교육의 필요성을 인지하고 있지만 평가, 경쟁적 분위기, 과중한 업무, 시간 부족의 이유로 인성교육을 실천하는데 어려움을 느끼고 있다. 이러한 선행 연구들은 실제로 인성교육을 수업에 적용한 경험이 없는 교사를 대상으로 한 것이 대부분이다. 인성교육 프로그램을 수업에 적용한 경험이 있는 교사들에 대한 연구가 많지 않기 때문에 실제로 과학 수업에서 인성교육 프로그램을 적용했을 때 발생하는 문제와 제안점을 논의하는 연구가 흔치 않다. 이 연구는 선행연구에서 그 효과가 논의된 인성역량 함양을 위한 협력적 문제해결(CoProC; Collaborative Problem-Solving for Character Competence) 프로그램을 수업 현장에 적용한 교사들의 실천에 대한 것이다. 과학 수업에 CoProC 프로그램을 적용한 5명의 교사가 연구에 참여하였다. CoProC 프로그램 수업에 참여한 학생들의 인성역량 성취도와 교사들의 두차례 인터뷰를 분석하여 결과를 도출하였다. 연구결과에 따르면 교사의 일반 교직 경력보다는 CoProC 프로그램에 대한 연수, 개발, 수업 경험이 학생의 인성역량 성취에 영향을 미치고, CoProC 프로그램의 목적에 대한 교사의 이해가 수업에서 어려움, 평가, 피드백에 영향을 미친다.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.