• Journal of The Korean Association of Information Education
• /
• v.24 no.5
• /
• pp.483-494
• /
• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.

• 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.

• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.209-214
• /
• 2024

This is an extended research paper focusing on the applications of Machine Learing and Artificial Intelligence in virtual learning environment. The world is moving at a fast pace having the application of Machine Learning (ML) and Artificial Intelligence (AI) in all the major disciplines and the educational sector is also not untouched by its impact especially in an online learning environment. This paper attempts to elaborate on the benefits of ML and AI in E-Learning (EL) in general and explain how King Khalid University (KKU) EL Deanship is making the best of ML and AI in its practices. Also, researchers have focused on the future of ML and AI in any academic program. This research is descriptive in nature; results are based on qualitative analysis done through tools and techniques of EL applied in KKU as an example but the same modus operandi can be implemented by any institution in its EL platform. KKU is using Learning Management Services (LMS) for providing online learning practices and Blackboard (BB) for sharing online learning resources, therefore these tools are considered by the researchers for explaining the results of ML and AI.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.487-492
• /
• 2022

Universities are looking for various methods to enhance educational competence level suitable for the rapidly changing social environment. This study suggests a method to promote academic and educational achievements by reducing drop-out rate from their majors through implementation of pre-survey of satisfaction that revised and complemented survey items. To supplement the CQI method implemented after a general satisfaction survey, a pre-survey of satisfaction was carried out. To consolidate students' competences, this study made prediction and analysis of data with more importance possible using the Random Forest of the machine learning technique that can be applied to AI Medici platform, whose design is underway. By pre-processing the pre-survey of satisfaction, the students information enrolled in classes were defined as an explanatory variable, and they were classified, and a model was created and learning was conducted. For the experimental environment, the algorithms and sklearn library related in Jupyter notebook 3.7.7, Python 3.7 were used together. This study carried out a comparative analysis of change in educational satisfaction survey, carried out after classes, and trends in the drop-out students by reflecting the results of the suggested method in the classes.

• 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.6
• /
• pp.961-972
• /
• 2021

In this study, an educational program was developed so that artificial intelligence could be used as a transdisciplinary convergence education with other disciplines. The main educational content is designed for 8 hours using machine learning to help students understand the molecular structure dealt with in high school chemistry. The program developed in this study calculated the I-CVI (Item Content Validity Index) value through expert review, and as a result, none of the items were rejected with a score of .80 or higher. Because the program of this study combines the content elements of the chemistry subject and the information (artificial intelligence) subject academically, it is expected that the learner will be able to increase the convergence talent literacy. In addition, since it is not required to secure a additional number of hours for this educational program, the burden on teachers may be low.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.11
• /
• pp.107-114
• /
• 2022

In this paper, proposes a delivery robot that can be autonomous driving learning. The proposed robot is designed to be used in park-type apartments without ground parking facilities. Compared to the existing apartments with complex ground and underground routes, park-type apartments have a standardized movement path, allowing the robot to run stably, making it suitable for students' initial education environment. The delivery robot is configured to enable delivery of parcels through machine learning technology for route learning and autonomous driving using cameras and LiDAR sensors. In addition, the control MCU was designed by separating it into three parts to enable learning by level, and it was confirmed that it can be used as a delivery robot for learning through operation tests such as autonomous driving and obstacle recognition. In the future, we plan to develop it into an educational delivery robot for various delivery services by linking with the precision indoor location information recognition technology and the public technology platform of the apartment.

• Journal of Convergence for Information Technology
• /
• v.12 no.5
• /
• pp.139-149
• /
• 2022

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

• Journal of The Korean Association of Information Education
• /
• v.26 no.1
• /
• pp.65-73
• /
• 2022

In this study, by using Entry text model learning, educational contents for artificial intelligence education of elementary school students are developed and applied to actual classes. Based on the elementary and secondary artificial intelligence content table, the achievement standards of practical software education and artificial intelligence education will be reconstructed.. Among text, images, and sounds capable of machine learning, "production of emotion recognition programs using text model learning" will be selected as the educational content, which can be easily understood while reducing data preparation time for elementary school students. Entry artificial intelligence is selected as an education platform to develop artificial intelligence education contents that create emotion recognition programs using text model learning and apply them to actual elementary school classes. Based on the contents of this study, As a result of class application, students showed positive responses and interest in the entry AI class. it is suggested that quantitative research on the effectiveness of classes for elementary school students is necessary as a follow-up study.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.285-301
• /
• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.