• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.547-554
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• 2019

With the curriculum revision in 2015, informatics for secondary high schools was designated as mandatory. As a result, there is an increasing interest in programming in elementary and junior high schools as well as in universities. Arduino is one of the famous tools for programming education, and the usefulness of it has been proven through various case studies. However, existing Arduino-based kits have hardware-dependent drawbacks such as complicated wiring, poor scalability, etc. To overcome these problems, we proposed a kit design, which has a module-based structure, can be extended through one common interface, and can be used for learning at various levels. In this paper, we describe the implementation details of FRUTO kit and a software to use it, which satisfies the proposed design criteria. FRUTO kit has been determined in its current form through several design changes, and is under pre-test before launching.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.751-759
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• 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 Engineering Education Research
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• v.24 no.4
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• pp.21-29
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• 2021

The objective of this research is to devise a BARS evaluation system as a performance evaluation plan for non-face-to-face capstone design and to verify the validity through the expert FGI as the remote education is highlighted as a new normal standard in the post corona epoch. The conclusion of this research is as follows. First, the non-face-to-face capstone design is a competency centered subject that allows you to develop the engineering and majoring knowledge and its function and attitude, and the achievement of program outcome is the objective competency, and the researcher proposes the BARS method evaluation, one of competency evaluation method, as a new performance evaluation plan. Second, for the evaluation of PO achievement of non-face-to-face capstone design, the researcher deduced 20 behavior identification standard(anchor) of BARS evaluation system, and developed the achievement standard per 4 levels. Third, as the evaluation tool of non-face-to-face capstone design, the presentation data(PPT), presentation video, product such as trial product(model), non-face-to-face class participation video, discussion participating video, team activity report, and result report for the evidential data of BARS evaluation were appeared as proper. Finally, the BARS evaluation plan of non-face-to-face capstone design would be efficiently made through the establishment of evaluation plan, the establishment of grading standard of BARS evaluation scale, the determination of evaluation subject and online BARS evaluation site.

• Journal of Platform Technology
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• v.9 no.3
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• pp.63-69
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• 2021

Limited materials to make coins for countries and designs suitable for hand-carry make the shape, size, and color of coins similar. This similarity makes that it is difficult for visitors to identify each country's coins. To solve this problem, we propose the coin classification method using CNN effective to image processing. In our coin identification method, we collect the training data by using web crawling and use OpenCV for preprocessing. After preprocessing, we extract features from an image by using three CNN layers and classify coins by using two fully connected network layers. To show that our model designed in this paper is effective for coin classification, we evaluate our model using eight different coin types. From our experimental results, the accuracy for coin classification is about 99.5%.

• Korean Journal of Occupational Health Nursing
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• v.28 no.4
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• pp.208-220
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• 2019

Purpose: This study aimed to determine health problems experienced by workers in special employment relationships (WSER) and identify coping strategies used when such problems occur. Methods: This qualitative study used the focus group interview method. Thirteen study participants included five home-visit tutors, five insurance salespersons, and three credit card recruiters. The interviews were conducted from November 2018 through January 2019, with each occupational group interview lasting about 2 hours. Analysis based on phenomenological research was independently performed by two researchers. Results: Most participants had common health problems involving vocal cord symptoms, and stress related to emotional labor and traffic accidents. The unique health problems included cystitis, musculoskeletal, and digestive symptoms in home-visit tutors; reduced vision and hearing in insurance salespersons; and mental distress in credit card recruiters. There was no protection system for their health coverage, and the company emphasized their self-employed status to avoid taking responsibility for them. Twelve participants did not purchase occupational accident insurance owing to both not having adequate information and economic burden concerning premium status. Conclusion: WSER experienced both physical and mental health problems. These problems were caused by their unstable employment status, and the social security system for their coverage being non-functioning.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.19-26
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• 2019

Recently, the importance of the internet of things (IoT) education has been emphasized due to the progress of research on IoT technology. Therefore, universities require an efficient IoT course. In this paper, we propose an undergraduate IoT course using the Bluetooth function of smartphone and Arduino kit. The proposed embedded IoT class uses the Bluetooth capabilities of the smartphone to connect Arduino and activate various sensors to encourage students to become interested in the class. According to students' midterm and final exam scores, students programming skills have been improved since students' projects were in progress during the course. Further, according to students' survey, the proposed IoT class is very effective in understanding the embedded IoT and 75% of the students satisfied with the proposed course.

• Journal of the Korean Chemical Society
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• v.65 no.1
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• pp.25-36
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• 2021

This study aimed to analyze achievement characteristics of Korean students in the results of PISA 2018 science domain. To this end, the characteristics of PISA 2009 to PISA 2018 science were analyzed in terms of the percentage of each performance level and the ratio of male and female by achievement level; in addition, the percentage of correct answers by framework subscale was compared with PISA 2015. The results showed that Korea has a higher percentage of students at the lower level of achievement as compared to the high-ranking countries of PISA, and the ratio of students at the higher level of achievement was lower. On average, the difference in achievement between boys and girls was negligible; however, but at the higher achievement level, the ratio of boys continued to be higher than that of girls. In addition, in the PISA science framework, the percentage of correct answers of the questions corresponding to 'personal' of 'contexts', 'evaluate and design scientific enquiry' of 'competencies', 'epistemic' of 'knowledge', and 'high' of 'cognitive demand' increased; similarly, and achievement improved as compared to PISA 2015. Based on these results of the study, we propose a method for improving teaching and evaluation to foster Korean students' scientific competence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.77-82
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• 2020

As Korea enters an aging age, social efforts to improve the IADL of the elderly are increasing. In this study, to improve the performance of financial management activities that the elderly is particularly burdened, we aim to learn the elderly through ATM simulation education contents so that they can use ATM smoothly. To this end, interviews were conducted with seniors to derive four major financial activities (deposits, withdrawals, deposit inquiries and bank account arrangements), and developed tablet PC-based ATM education contents identical to the existing bank ATM interfaces. The experiment was conducted on 20 elderly people in the Elderly Day Care Center, and their satisfaction, fatigue and performance were measured before and after education. The results of this study can provide ATM design guidelines for the elderly who have difficulty using ATM.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.215-220
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• 2021

In order to reduce the harmful effects on the human body caused by the recent increase in the generation of fine dust in Korea, there is a need for technology to help predict the level of fine dust and take precautions. In this paper, we propose a 1D Convolutional-Recurrent Neural Network (1-D CRNN) model to predict the level of fine dust in Korea. The proposed model is a structure that combines the CNN and the RNN, and uses domestic and foreign fine dust, wind direction, and wind speed data for data prediction. The proposed model achieved an accuracy of about 76%(Partial up to 84%). The proposed model aims to data prediction model for time series data sets that need to consider various data in the future.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.