• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.

• Journal of Korean Elementary Science Education
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• v.40 no.3
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• pp.283-294
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• 2021

The purpose of this study is to examine the trends of science gifted education-related research for the last 5 years using LDA topic modeling. To achieve the purpose of the study, 2,404 keywords of 292 domestic academic papers were analyzed using RISS, KISS, and DBpia. The main results were as follows. First, the number of researches in science gifted education has been decreasing since 2019. In the science gifted education research, the top 10 keywords were 'students', 'program', 'elementary school', 'class', 'creativity', 'gifted education', 'awareness', 'teacher', 'education', and 'activity'. Second, as a result of topic modeling analysis, 10 topics were derived. Research topics mainly conducted in science gifted education for the past five years are 'Affective characteristics of science gifted students', 'Characteristics of science gifted students in middle school', 'Development and application of science gifted education programs', 'Education programs of science gifted high school', 'Cognitive characteristics of science gifted students', 'Policy of science gifted education', 'Science gifted students and creativity', 'Research conducting education by science gifted students', 'Academic and career choice of science gifted students', 'Science concept of science gifted Students'. In the past, the proportion of specific topics was relatively high, but the proportion between topics does not differ significantly as 2019 approaches. Therefore, it can be confirmed that the more recent it comes, the more research is being conducted evenly without being biased toward one subject.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.265-267
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• 2018

4차 산업혁명은 2016년 스위스 다보스에서 열린 세계경제포럼(WEF)의 주요 이슈였다. 4차 산업혁명 시대에서 요구되는 IT융합 기술에는 사물인터넷 환경에서 모든 사물이 사이버 세계를 통하여 연결되어 정보를 수집하고 축적하며, 축적된 빅데이터를 기반으로 인공지능 알고리즘을 활용하는 기술 등이 필요하다. 이와 같은 IT융합 기술과 관련하여 4차 산업혁명 시대를 대비하여 산업체에서 필요한 실무적인 IT융합 인력 양성이 컴퓨터 관련학과의 주요 교육 내용으로 요구되고 있다. 이러한 시대적 상황에서 하나의 접근 방법으로 대두되는 것이 NCS 기반의 교육과정에 기반을 둔 IT융합 인력의 양성이다. 이 논문에서는 우리나라 산업체에서 요구하는 IT융합 인력 양성을 위하여 컴퓨터과학 전공을 위한 NCS 교육과정을 제시하고 분석한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.685-694
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• 2009

The idea of this paper is to get the time series data from the number of student on the elementary, meddle and high-school for the forecasting of the numbers of student. Tow models, model A and model B, of time series data are obtained. The Holt-Winters additive methods are used for the forecasting of the numbers of student with the model A and model B until 2019 year. As the result, the abilities of forecasting on model A and B are better than those of the Korean education statistical system 2007.

• Journal of The Korean Association of Information Education
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• v.20 no.6
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• pp.543-552
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• 2016

Recently, a number of researches focus on social network analysis and it is applied to various fields not only in social science area but also in natural science area. Therefore, the social network analysis and the text analysis were conducted in order to analyze the current trend of the theses in information education field. The result indicated that the most frequently mentioned words were consistent with the development of information technology and the change in information education curriculum. That is, the mentioned words were computer aided instruction (CAI) and courseware for period 1, ICT for period 2, smart and scratch for period 3, and in period 4, computational thinking ability and coding appeared for the first time. Moreover, as the result of social network analysis, it concluded the research topics became more complicated and detailed as the words diversified throughout the period in which the simplified network in period 1 changed its configuration into a structure with more diversified words of higher centrality.

• Journal of Korean Elementary Science Education
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• v.42 no.1
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• pp.109-126
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• 2023

In this study, I took the evidence-explanation (E-E) continuum perspective to examine the epistemological implications of scientific reasoning cases designed by preservice elementary teachers during their simulation teaching. The participants were four preservice teachers who conducted simulation instruction on the seasons and high/low air pressure and wind. The selected discourse episodes, which included cases of inductive, deductive, or abductive reasoning, were analyzed for their epistemological implications-specifically, the role played by the reasoning cases in the E-E continuum. The two preservice teachers conducting seasons classes used hypothetical-deductive reasoning when they identified evidence by comparing student-group data and tested a hypothesis by comparing the evidence with the hypothetical statement. However, they did not adopt explicit reasoning for creating the hypothesis or constructing a model from the evidence. The two preservice teachers conducting air pressure and wind classes applied inductive reasoning to find evidence by summarizing the student-group data and adopted linear logic-structured deductive reasoning to construct the final explanation. In teaching similar topics, the preservice teachers showed similar epistemic processes in their scientific reasoning cases. However, the epistemological implications of the instruction were not similar in terms of the E-E continuum. In addition, except in one case, the teachers were neither good at abductive reasoning for creating a hypothesis or an explanatory model, nor good at using reasoning to construct a model from the evidence. The E-E continuum helps in examining the epistemological implications of scientific reasoning and can be an alternative way of transmitting scientific reasoning.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.31 no.10
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• pp.54-59
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• 2013

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.6
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• pp.59-64
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• 2012

• Journal of Information Management
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• v.39 no.3
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• pp.23-48
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• 2008

The purpose of this study is to propose the mandatory and extensible elements of Korea Education Metadata elements for digital course resources universities in Korea. For the study, literature research, case study and interview were performed. The mandatory elements of KEM consisted of sixty nine elements, and were proposed by two phases. Also, based on interview process twelve items were newly recommended to supplement KEM elements.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.