• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.567-577
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• 2022

Interest in AI and SW education is growing as digital literacy is emphasized in the revised elementary school curriculum for 2022. There are numerous restrictions on how pupils can enhance their digital literacy because there are only 34 class hours available for information education in elementary schools. Therefore, other subjects and information education must be blended in order to ensure class hours for AI and SW instruction. In this study, we investigated the impact of S-PUMA reading instruction on the literary imagination and computational thinking of elementary school pupils. To conduct this study, two classes of sixth graders in an elementary school were chosen and split into an experimental group and a control group. Over the course of five sessions, only the experimental group received reading instruction using the S-PUMA teaching approach. It was discovered that reading instruction with the S-PUMA teaching methodology enhanced literary imagination and computational thinking. Further study is required to identify whether the improvement in creative imagination, a component of literary imagination, is a result of the S-PUMA teaching approach or a natural result of the subject matter of the lesson.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.11-22
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• 2022

The educational environment, where the change to blended learning and AI convergence education through non-face-to-face is accelerating, is based on the cultivation of digital literacy. This study attempted to verify the effectiveness of future competencies by creating a collaborative SW education program on the metaverse platform that emerged by supplementing the problems through non-face-to-face. Twenty programs on how to design and create software were organized for small-scale elementary classes in the metaverse. In order to verify the effectiveness 4C competency tool presented as future educational competency was selected, and homogeneity test for the experimental group and t-test were conducted. The results showed the SW education programs based on metaverse was effective in improving collaborative communication skills, confirming the possibility of SW education through blended learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.59-69
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• 2022

The purpose of this study is to suggest improvement directions by analyzing the meaning of the 2022 revised curriculum. Research methods include literature research, surveys, and interviews. The conclusion is as follows. First, The background of the promotion has been revised to cultivate the competencies necessary for the future society and to strengthen the learner-tailored education. Second, what characterizes the 2022 revised curriculum is that it is being created in collaboration with people as a future-oriented curriculum for the first time in history. Third, the implementation of the 2022 revised curriculum is being directed towards individuality and diversity, decentralization and autonomy, digitally based education, and public performance and accountability. Fourth, the principal contents are curriculum innovation in response to future changes, cultivating community values and capacity building for learners, strengthening education for elementary, middle, and high school students to develop digital and AI literacy, and strengthening the curriculum for all.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.449-455
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• 2022

The digital culture area is a field of artificial intelligence education and partially overlaps with information ethics, and this study proposed an elementary school-centered digital culture curriculum, a detailed area of artificial intelligence education. First, as a leading overseas case in the field of digital culture, we analyzed the secure online interaction, which is the core content of the K12CS framework in the United States and the UK's computing curriculum. We presented by categorizing it into four digital areas: content, communication method, copyright, and job exploration in accordance with the domestic educational environment and field conditions. In addition, educational goals were stated according to the level of kindergarten and elementary school, and the validity of a group of field teachers majoring in in information and artificial intelligence was investigated to confirm the validity. As a result of the analysis, it was found that all the contents set were suitable except for some of the detailed goals of the first grade elementary school stages. Accordingly, a digital culture curriculum for artificial intelligence education was presented by revising and supplementing the detailed goals of the first graders.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

• Magazine of the Korea Concrete Institute
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• v.2 no.4
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• pp.83-90
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• 1990

Ultra Rapid Hardening Cement (LJRHC) makes immediate response, cracks and finally collapses in the 5 % acid solution. Such physical and chemical reaction is assumed to occur beCaUSE) it contains calcium aluminate( 11 Cao . 7AI,O, . CaF,) and the hydrate. This experimental study aims to improve URHC by making up for its weakness, which appears when its pro¬perties are compared with other cements properties, in the Polymer Cement Mortar(PCM) The result are : PCM using URHC proved to be inferior to PCM using other cements in the resistance to the acid in that the former cracked and collapsed after 10 days, and 22 days, in P/C=O%, and 5% respectively. And in P/C=15% and 20 % the PCM using URHC proved to be more resistant to the acid.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.123-123
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• 2018

가뭄은 홍수와 달리 진행속도가 비교적 느리기 때문에 초기에 감지한다면 피해를 최소화 할 수 있다. 국내에서는 가뭄전망을 위해 물리적 기반의 기상-수문연계해석 시스템을 구축하여 월 내지 계절전망을 수행하고 있다. 물리적 기반의 가뭄전망은 수치예보모델의 불확실성을 가지고 있으므로 예보 정확도 개선의 측면에서는 통계적 모델을 같이 활용하는 것이 바람직하다. 최근 국외에서는 통계적 방법인 AI (Artificial Intelligence) 기술을 사용하여 가뭄을 전망하는 연구가 활발히 진행 중이나, 아직까지 국내에서는 관련연구가 미흡한 실정이다. 이에 본 연구에서는 ANFIS (Adaptive Neuro-Fuzzy Inference System) 기반의 댐 유입량 예측 모델을 구축하고 SRI (Standardized Runoff Index)를 활용하여 수문학적 가뭄전망을 수행하였다. 대상유역은 국내 주요 다목적댐이 위치한 충주댐 유역과 소양강댐 유역을 선정하였다. 수문 및 기상자료는 국토 교통부 및 기상청의 관측 댐 유입량, 관측 강수량, 관측 기온 및 장기기상예보 자료를 사용하였다. ANFIS 모델 구축을 위한 훈련 및 보정기간과 검정기간은 각각 1987~2010년과 2011~2016년을 선정하였다. 수문학적 가뭄전망은 지속기간 3개월의 1개월 전망 SRI3를 활용하였으며, SRI3는 관측유입량과 예측유입량을 결합하여 산정하였다. 댐 예측유입량 및 수문학적 가뭄전망의 정확도 평가를 위해 상관계수, 평균제곱근오차를 활용하였다. 댐 예측유입량 평가 결과 예측값과 관측값의 상관계수가 높게 나타났으며, 평균제곱근오차는 낮아 예측성이 뛰어났다. SRI3의 경우 관측값과 예측값의 가뭄발생시기가 유사하여 가뭄을 적절하게 반영하는 것으로 나타났다. 본 연구의 결과는 통계적 기반의 수문학적 가뭄전망기법을 개발하였다는 측면에서 의의가 있으며, 향후 물리적 기반의 가뭄전망정보와 결합한다면 보다 실효성이 향상될 것으로 기대된다.

• 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.