• Research in Mathematical Education
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• v.27 no.2
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• pp.223-239
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• 2024

The integration of coding and mathematics education, known as coding-integrated mathematics education, has received much attention due to the strength of Artificial Intelligence-based Science, Technology, Engineering, Arts, and Mathematics (AI-based STEAM) education in improving students' affective domain. The present study investigated the effectiveness of coding-integrated mathematics education on students' development of affective mathematics engagement. Participants in this study were 86 middle and high school students who attended the coding-integrated mathematics program. Surveys of students' affective mathematics engagement were administered before and after the intervention period. The results showed that students' affective mathematics engagement was statistically significantly improved through coding-integrated mathematics education. In particular, students exhibited increased positive affective mathematics engagement in terms of mathematical attitude, emotion, and value. These findings indicate the positive influence of coding-integrated mathematics education on students' learning in mathematics.

• Journal of Korean society of Dental Hygiene
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• v.21 no.1
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• pp.5-16
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• 2021

Objectives: To evaluate the impact of an oral exercise program including whole-body exercises on oral function in older people. Methods: The participants (aged ≥65 years) were divided into three groups: intervention group I (only oral exercise), intervention group II (oral exercise with whole-body exercises), and control group (no intervention). The oral health status, saliva flow rate, and oral muscle strength were evaluated. Analyses were performed to compare the three groups and identify the changes in the aforementioned parameters before and after the program. Results: The saliva flow rate significantly increased in intervention groups I and II after the program. Oral muscle strength evaluation using the Iow a oral performance instrument showed that the anterior tongue strength increased significantly in intervention group I; the posterior tongue strength and cheek strength also increased but not significantly. The anterior tongue, posterior tongue, and cheek strengths significantly increased in intervention group II. Conclusions: The oral exercise program including whole-body exercises showed positive effects on the saliva flow rate and oral strength. No significant differences were observed in the quality of life related to oral health.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.139-149
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• 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 Korea Multimedia Society
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• v.25 no.11
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• pp.1643-1652
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• 2022

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.

• Annual Conference of KIPS
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• 2021.11a
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• pp.749-752
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• 2021

오늘날 인공지능(Artificial Intelligence, AI) 분야에서 가장 구현하기 어려운 분야 중 하나는 추론이다. 근래 추론 분야에서 영상과 언어가 결합한 다중 모드(Multi-modal) 환경에서 영상 기반의 질의 응답(Visual Question Answering, VQA) 과업에 대한 AI 모델이 발표됐다. 얼마 지나지 않아 VQA 모델의 성능을 개선한 GVQA(Grounded Visual Question Answering) 모델도 발표됐다. 하지만 아직 GVQA 모델도 완벽한 성능을 내진 못한다. 본 논문에서는 GVQA 모델의 성능 개선을 위해 VCC(Visual Concept Classifier) 모델을 ViT-G(Vision Transformer-Giant)/14로 변경하고, ACP(Answer Cluster Predictor) 모델을 GPT(Generative Pretrained Transformer)-3으로 변경한다. 이와 같은 방법들은 성능을 개선하는 데 큰 도움이 될 수 있다고 사료된다.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.401-408
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• 2023

In this study, a career exploration program was developed for athletic students. Therefore, existing research on career exploration for athletics was analyzed, requirements were identified, and a learning plan was designed. Based on this, a step-by-step educational program was developed. In addition, since research on career exploration for athletic students was not active in previous studies, 'problem definition' - 'data collection' - 'data preprocessing' - 'data analysis' by referring to existing career exploration studies that were studied in the school field. - 'Data visualization' - 'Simulation analysis' were divided into stages to conduct the study. Through this study, it is expected that research on vocational education for athletic students will be more active.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.125-131
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• 2020

As technology advances, the importance of software education is increasing. Accordingly, interest in information subjects is increasing, but intending elementary learners to show algorithms only for specialized IT skills that could spoil the interest. In this paper for the elementary school students, through the four stages, 2015 revision curriculum analysis, creating of training program development operating plans, applying programs for the targeting students and analysis of results and evaluation, using Rich Picture technique which is various tools such as pictures and speech bubble symbols for the learners can express the intelligent CCTV algorithm freely and easily so they can understand fully about the algorithm of intelligent CCTV that uses artificial intelligence to extract faces from subjects. Suggest on this paper, the proposal of educational program can help the learner to grasp the principle of the algorithm by using the flowchart. As the result, Through the modification and development of the proposed program, we will conduct research on IT creative education that can be applied in various areas.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.329-340
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• 2021

Purpose: In this paper, using artificial neural network algorithm, the Korean Peninsula was analyzed for drought vulnerable areas by predicting weather data changes. Method: Monthly cumulative precipitation data were utilized for research areas considering the specific nature areas, and weather data prediction through artificial neural network algorithm was carried out using statistical program R. The predicted data were applied to the Standardized Precipitation Index (SPI) to analyze drought vulnerable areas in the Korean Peninsula. Result: In this paper, the correlation coefficient values between real and predicted data are found to be 0.043879 higher on average than the regression results, using artificial neural network algorithms. Conclusion: The results of the research are expected to be used as basic research materials for responding to drought.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.77-82
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• 2023

Intelligence Home and Home Automation, which attracted attention before Smart Home, caused inconvenience to users by focusing on applying cutting-edge technologies to homes, and failed to popularize them due to lack of unemployment efficiency. However, with the 4th Industrial Revolution, various services using technologies related to big data, artificial intelligence, and the Internet of Things are increasing, and the rate of smart home services that operate, manage, and automate products at home is gradually increasing. In line with this trend, this paper implements a program app that can connect, manipulate, and manage products using MQTT server, Django web framework, and WIFI communication module.