• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.803-812
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• 2021

This study explored the concept of artificial intelligence and the problem-solving process that can be explained through literature research. Through this study, the educational meaning and application plan of artificial intelligence that can be explained were presented. XAI education is a human-centered artificial intelligence education that deals with human-related artificial intelligence problems, and students can cultivate problem-solving skills. In addition, through algorithmic education, it is possible to understand the principles of artificial intelligence, explain artificial intelligence models related to real-life problem situations, and expand to the field of application of artificial intelligence. In order for such XAI education to be applied in elementary schools, examples related to real world must be used, and it is recommended to utilize those that the algorithm itself has interpretability. In addition, various teaching and learning methods and tools should be used for understanding to move toward explanation. Ahead of the introduction of artificial intelligence in the revised curriculum in 2022, we hope that this study will be meaningfully used as the basis for actual classes.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.573-584
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• 2020

This study is about the development of AI algorithm education program using cognition modeling to positively improve students' image on AI. First, we analyzed the concept of user-based collaborative filtering and developed the education program using the cognition modeling method. We checked the adequacy of program through the expert validity test. Both CVR values for the content development method of cognitive modeling and the developed program showed validity above .80. We applied the developed program to elementary school students in class. The test was conducted using a semantic discrimination to examine changes in students' perception of artificial intelligence before and after. We were able to confirm that the students' AI images were significant positive change in 12 of the 23 words in the adjective pair.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.106-117
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• 2022

In 2021, AI basics were introduced in the high school curriculum. There are many worries that the problem of utilization-oriented education will be repeated with the introduction of artificial intelligence education rather than the principles that occurred when ICT was applied to education in the past. Most of the existing AI education platforms focus only on the use of AI. For artificial intelligence education of middle school students, there are difficulties in learning about the process by which artificial intelligence derives results and learning the principles of artificial intelligence algorithms. Recently, as the educational application of metaverse has become a hot topic, research has been started to improve learning achievement by arousing students' immersion and interest. This research developed educational game contents about decision tree algorithm using metaverse as educational contents that can be used in middle school AI education. By applying games to education, it was intended to increase students' interest and immersion in artificial intelligence, and to increase educational effectiveness. In this paper, the educational effectiveness, difficulty, and level of interest were analyzed for pre-service teachers regarding the developed game content. Based on this, a future principle-oriented artificial intelligence education method was suggested.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.295-305
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• 2022

In the modern artificial intelligence society, the goal of education is to cultivate convergent talents who can adapt well to and lead the artificial intelligence society. The core competency required of convergent future talents is to be able to understand and create software well. In this respect, algorithmic education is very important. Therefore, in this study, an algorithm education program was developed to improve convergent thinking ability. The effectiveness was verified through the paired samples t-test by conducting a pre-post test of convergent thinking ability, and it was found that students' convergent thinking ability was significantly improved in the areas of algorithm knowledge and function.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.173-178
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• 2020

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. An algorithmic education focused on problem solving and learning is efficient for computer science education. In this study, the results of an assessment of computational thinking at the beginning of the semester, a satisfaction survey at the end of the semester, and academic performance were compared and analyzed for 28 students who received algorithmic education focused on problem-solving learning. As a result of diagnosing students' computational thinking and problem-solving learning, teaching methods, lecture satisfaction, and other environmental factors, a correlation was found, and regression analysis confirmed that problem-solving learning had an effect on improving lecture satisfaction and computational thinking ability. For algorithmic education, if you pursue a problem-solving learning technique and a way to improve students' satisfaction, it will help students improve their problem-solving skills.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.813-824
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• 2021

To improve educational performance by analyzing the learning trends of adult learners of Open High Schools, various algorithm models using artificial intelligence were designed and performance was evaluated by applying them to real data. We analyzed Log data of 115 adult learners in the cyber education system of Open High Schools. Most adult learners of Open High Schools learned more than recommended learning time, but at the end of the semester, the actual learning time was significantly reduced compared to the recommended learning time. In the second half of learning, the participation rate of VODs, formation assessments, and learning activities also decreased. Therefore, in order to improve educational performance, learning time should be supported to continue in the second half. In the latter half, we developed an artificial intelligence algorithm models using Tensorflow to predict learning time by data they started taking the course. As a result, when using CNN(Convolutional Neural Network) model to predict single or multiple outputs, the mean-absolute-error is lowest compared to other models.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.757-760
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• 2014

프로그래밍 능력은 21세기 정보화 사회를 살아가는데 있어 인간이 컴퓨터를 활용하여 다양한 문제를 해결할 수 있도록 도움을 준다. 효과적인 프로그래밍 교육이 이루어지기 위해서는 학습자들에게 순차적 수행, 조건적 수행, 반복적 수행과 같은 기본적인 프로그래밍 개념을 습득하도록 할 필요가 있다. 따라서 본 연구는 스도쿠 인공지능 프로그래밍 교육에서 프로그래밍의 기본 개념을 바탕으로 알고리즘을 설계하는 방법을 학습시키는 방안을 모색하기 위한 목적이 있다. 연구의 목적을 달성하기 위해 중학생 10명을 대상으로 실험 연구를 진행하였다. 연구 결과, 학습자는 연구자가 제안한 활동지가 알고리즘 설계 학습에 도움이 된 것으로 인식한 것을 확인할 수 있었다. 본 연구는 프로그래밍 교육에서 초보 학습자가 이해하기 어려워하는 프로그래밍 개념을 학습하는데 도움이 되는 학습 방법을 제시하였다는데 의의가 있다.

• Proceedings of The KACE
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• 2017.08a
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• pp.209-211
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• 2017

기계학습은 인공지능(AI, Artificial Intelligence)의 일종으로 다른 인공지능 알고리즘이 정해진 규칙을 기반으로 주어진 임무(Task)를 해결하는 것과는 달리, 기계학습은 수집된 Data를 기반으로 최적의 솔루션을 학습한 후 미래의 값들을 예측하거나 해석하는 방법을 사용하고 있다. 더욱이 인터넷을 통한 연결성의 확대와 컴퓨터의 연산능력 발전으로 가능하게 된 Big-Data를 기반으로 하고 있어 이전의 인공지능 알고리즘에 비해 월등한 성능을 보여주고 있다. 그러나 기계학습 알고리즘이 Data를 학습할 때 학습 결과를 사람이 해석하기에 너무 복잡하여 사람이 그 내부 구조를 이해하는 것은 사실상 불가능하고, 이에 따라 학습된 기계학습 모델의 단점 또는 한계 등을 알지 못하는 문제가 있다. 본 연구에서는 이러한 블랙박스화된 기계학습 알고리즘의 특성을 이해하기 위해, 기계학습 알고리즘이 특정 입력에 대한 결과를 예측할 때 어떤 입력들로 부터 영향을 많이 받는지 그리고 어떤 입력으로부터 영향을 적게 받는지를 알아보는 방법을 소개하고 기존 연구의 단점을 개선하기 위한 방법을 제시한다.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.65-70
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• 2024

As the interest in AI education grows, researchers have made efforts to implement AI education programs. However, research targeting pre-service teachers has been limited thus far. Therefore, this study was conducted to develop an AI literacy education program for preservice secondary teachers. The research results revealed that the weekly topics included the definition and applications of AI, analysis of intelligent agents, the importance of data, understanding machine learning, hands-on exercises on prediction and classification, hands-on exercises on clustering and classification, hands-on exercises on unstructured data, understanding deep learning, application of deep learning algorithms, fairness, transparency, accountability, safety, and social integration. Through this research, it is hoped that AI literacy education programs for preservice teachers will be expanded. In the future, it is anticipated that follow-up studies will be conducted to implement relevant education in teacher training institutions and analyze its effectiveness.