• Journal of Practical Engineering Education
• /
• v.15 no.1
• /
• pp.31-38
• /
• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• Journal of The Korean Association of Information Education
• /
• v.25 no.2
• /
• pp.327-335
• /
• 2021

This study verified the effectiveness of machine learning education programs focused on data labeling as an educational method for improving computational thinking of elementary school students. The education program was designed and developed based on the results of a preliminary demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed education program, 17 sixth-grade students attending K Elementary School were given 2 classes per day for a total of 6 weeks. In order to measure the effect of the training on improving computational thinking, the educational effects were analyzed by conducting pre-post-inspection using the "Beaver Challenge". According to the analysis, machine learning education focused on data labeling contributed to improving computational thinking of elementary school students.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.633-634
• /
• 2020

본 논문에서는 소외계층 초등정보영재를 위한 온라인 머신러닝 교육 프로그램을 개발하였다. 교육 프로그램은 초등정보영재 전문가가 개발하였으며, 인공지능 교육 전문가가 검증하였다. 교육 프로그램은 15차시로 구성하였으며, 인공지능에 대한 이해, 데이터 수집 및 표현, 모델 선택, 훈련, 평가, 실생활 사례 제작, 예측으로 내용을 구성하였다. 교육 프로그램에서 학습 모형은 이재호와 홍창의(2009)의 문제 중심형 e-PBL 학습 모형을 본 연구에 맞게 수정하여 활용하였다. 향후 연구에서는 개발한 교육 프로그램을 소외계층 초등 정보영재에 적용하고, 교육 프로그램을 통한 소외계층 초등정보 영재의 변화를 분석하고자 한다.

• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.7-11
• /
• 2021

This study designs block-based machine learning education program using sound data that can be easily applied in elementary schools. The education program designed its goals and directions based on the results of a demand analysis conducted on 70 elementary school teachers in advance according to the ADDIE model. Scratch in Machine Learning for Kids was used for block-based programming, and the education program was designed to discover regularity of data values using sound data, learn the principles of artificial intelligence, and improve computational thinking in the programming process. In a later study, the education program needs to verify what changes there are in attitudes and computational thinking about artificial intelligence.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.719-722
• /
• 2021

최근 인공지능의 새로운 기술들이 하루가 다르게 발전하고 있다. 이에 본 연구에서는 인공지능 교육과 글쓰기 교육을 연계하여 초등학생들의 어휘력 향상을 위한 수업을 설계하고자 한다. 그 방법으로는 본 수업에 앞서 어휘 10문제를 테스트하여 실험에 참가하기 전의 어휘력을 점검한다. 그 후 머신러닝 포키즈를 이용하여 여러 감정에 해당되는 단어들을 다양하게 훈련하도록 하였고, 그 후 관련된 어휘 10문제를 다시 테스트 하였다. 실험 결과 실험에 참가하기 전에는 100점 만점에 58.8점으로 나왔으나 훈련 후의 결과는 평균 68점으로 모든 학생의 성적이 좋아지는 결과를 얻을 수 있었다. 어휘력 문항수가 적은 점과 10명의 실험참가자로 일반화할 수 없는 한계가 있다. 향후 초등교재 한권을 선정하여 어휘를 모두 분석한 후 가장 많이 등장하는 어휘를 골라내어 테스트하여 좀 더 통계적으로 의미 있는 분석을 하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.243-244
• /
• 2020

본 논문에서는 기존 AI 기능을 탑재한 교육용 프로그래밍 언어 기반의 서비스들의 문제점을 개선할 수 있는 머신러닝 학습을 위한 교육용 프로그래밍 언어 기반 실습 플랫폼을 제안한다. 이번 연구에서는 기존 교육용 프로그래밍 언어 기반 서비스의 대표주자인 Scratch 3.0과 Tensorflow를 접목하여 AI에 대한 높은 이해도를 가질 수 있도록 하는 학습 방향을 제시하고 Gray-Box 형태의 학습 모델 서비스를 구현한다.

• Journal of The Korean Association of Information Education
• /
• v.25 no.5
• /
• pp.705-712
• /
• 2021

This study aims to develop artificial intelligence education programs that can be easily applied in elementary schools according to the trend of the times called artificial intelligence. The training program designed the purpose and direction based on the analysis results of the needs of 70 elementary school teachers according to the steps of the ADDIE model. According to the survey, elementary school students developed a machine learning education program to set sound data as the theme of the most accessible in their daily lives and to learn the principles of artificial intelligence in solving problems using sound data in real life. These days, when the need for artificial intelligence education emerges, elementary machine learning education programs that solve daily life problems based on sound data developed in this study will lay the foundation for elementary artificial intelligence education.

• Journal of The Korean Association of Information Education
• /
• v.25 no.5
• /
• pp.751-759
• /
• 2021

This study developed an artificial intelligence education program using localized public data as an educational method for improving computing thinking skills of elementary school students. According to the ADDIE model, the program design was carried out based on the results of pre-requisite analysis for elementary school students, and textbooks and education programs were developed. Based on localized public data, the training program was constructed to learn the principles of artificial intelligence using machine learning for kids and scratches and to solve problems and improve computational thinking through abstracting public data for purpose. It is necessary to put this training program into the field through further research and verify the change in students' computational thinking as a result.

• Journal of The Korean Association of Information Education
• /
• v.24 no.1
• /
• pp.49-57
• /
• 2020

The society we are living in has being changed to the age of the intelligent information society after passing through the knowledge-based information society in the early 21st century. In this study, we have developed the instructional model for software education based on the machine learning which is a field of artificial intelligence(AI) to enhance the core competencies of learners required in the intelligent information society. This model is focusing on enhancing the core competencies through the process of problem-solving as well as reducing the burden of learning about AI itself. The specific stages of the developed model are consisted of seven levels which are 'Problem Recognition and Analysis', 'Data Collection', 'Data Processing and Feature Extraction', 'ML Model Training and Evaluation', 'ML Programming', 'Application and Problem Solving', and 'Share and Feedback'. As a result of applying the developed model in this study, we were able to observe the positive response about learning from the students and parents. We hope that this research could suggest the future direction of not only the instructional design but also operation of software education program based on machine learning.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.155-170
• /
• 2020

The purpose of this study was to apply machine learning techniques (Decision Tree, Random Forest, XGBoost) to data from the 4th~6th year of the Seoul Education Longitudinal Study to find the factors predicting the career maturity of middle school students in Seoul city. In order to evaluate the machine learning application result, the performance of the model according to the indicators was checked. In addition, the model was analyzed using the XGBoostExplainer package, and R and R Studio tools were used for this study. As a result, there was a slight difference in the ranking of variable importance by each model, but the rankings were high in 'Achievement goal awareness', 'Creativity', 'Self-concept', 'Relationship with parents and children', and 'Resilience'. In addition, using the XGBoostExplainer package, it was found that the factors that protect and deteriorate career maturity by panel and 'Achievement goal awareness' is the top priority factor for predicting career maturity. Based on the results of this study, it was suggested that a comparative study of machine learning and variable selection methods and a comparative study of each cohort of the Seoul Education Termination Study should be conducted.