• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.166-174
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• 2019

This study aims to determine factors influencing pre-service early childhood teachers' perception of technology equipment. Online survey is conducted to 289 students majoring in Early Childhood Education and Child Development located in city A, B, and C. SPSS 25 program analyzes 273 answers. The results show that technology education experience in high school influences TPACK and TPACK's sub-factor technology knowledge; and that technology education experience in college (university?) has a positive influence on computational thinking, perception of technology, TPACK, and TPACK's sub-factors-technology knowledge, early childhood education knowledge, and TPACK knowledge. In addition, perception of technology equipment shows high correlation with TPACK and computational thinking. Indeed, computational thinking and TPACK have 42.3% explanatory power on perception of technology equipment. The results imply that education system supporting computational thinking and TPACK should be prioritized for pre-service early childhood teacher to use technology effectively in the field.

• Journal of The Korean Association For Science Education
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• v.39 no.6
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• pp.777-790
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• 2019

The purpose of this study is to investigate the experience of teachers who apply Smart-technology in elementary school STEAM class and the reasons, difficulties when applying the technology and required support. Semi-structured in-depth interviews were conducted with six elementary school teachers with specialized knowledge in STEAM education who have experienced STEAM lessons several times before. The research findings are as follows: First, research participants utilized a variety of Smart-technology in STEAM class, most of which were experiential or interactive technology. Among the STEAM learning criteria, the Smart-technology in 'Creative Design' course was most often applied. Second, they adopted Smart Technology in STEAM class to encourage students to feel interested, actively participate in the class, enjoy indirect experience, and nurture interest in state-of-the-art technology. They used it to prepare for future societies and organize classes that are suitable for STEAM learning criteria. They also used Smart-technology because it was easy to use. Third, they found it difficult to find, secure, and use suitable Smart-technology when applying Smart-technology in the STEAM class. They also had trouble restructuring the curriculum. In addition, there were difficulties in using Smart-technology in the class such as lack of class hours, increased level of activity, insufficient physical environment and unexpected malfunction of Smart-technology, thus interrupted the class. After the class, it was hard to manage Smart-technology and also, there were difficulties in assessment, record, and negative awareness of surrounding people. Fourth, they mentioned that's suggesting education guidelines, develop, and distribute educational materials are required to enable 'Creative Design,' reduce educational content, provide training, secure Smart-technology equipment and provide Wi-Fi, support teacher's club and communities and create an atmosphere to emotionally support teachers in order to activate using Smart-technology in STEAM class.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.145-148
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• 2013

최근 디지털 기술의 고도화로 인하여 테크놀로지가 급속도록 발전하면서 다양한 정보통신 기술과 지식의 융합을 교육 환경에 적용하는 스마트러닝에 대한 관심이 집중되기 시작하였다. 이러한 스마트러닝 환경의 장점들을 통해 학생들이 더욱 효과적으로 교육받을 수 있게 되었다. 학생들은 모바일 기기의 접근성에 대한 인식이 확대되고 모바일 기기와 교육용 프로그램을 활용한 학습이 활성화 되고 있다. 이에 본 연구에서는 스마트기기를 이용한 학습과 평가가 이루어질 수 있는 방안에 대해 알아보고자 한다.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.47-56
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• 2020

The purpose of this study is to comprehensively analyze elementary school teachers' perceptions of the use of artificial intelligence in education. Recently, interest in the use of artificial intelligence has increased in the field of education. However, there is a lack of research on the perceptions of elementary school teachers using AI in education. Using descriptive statistics, multiple linear regression analysis, and semantic differential meaning scale, 69 elementary school teachers' perceptions of using AI in education were analyzed. As a results, artificial intelligence technology was perceived as most suitable method for assisting activities in class and for problem-based learning. Factors which influence the use of AI in education were learning contents, learning materials, and AI tools. AI in education had the features of personalized learning, promoting students' participation, and provoking students' interest. Further, instructional strategies or models that enable optimized educational operation should be developed.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.863-885
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• 2009

In the era of wireless internet, we are apt to use a mobile phone for learning mathematics, besides the pc computer and the notebook computer. This study was to investigate the effect of M-learning when students were given a wireless mobile phone in terms of their perspectives towards the use of a mobile phone, achievement and attitudes towards mathematics. They were the 3th grader in a high school, who were expected to take Aptitude Test for the entrance of the university level. The most students who took an ubiquitous environment of M-learning showed it as a benefit for learning mathematics and did not spend time at other activities such as listening to music, sending text-message, playing games, etc, but at the M-learning activities. The students who engaged in the M-learning activities were improved a significantly higher score at Aptitude Test than the students who took the make-up courses in the school and also did a significantly higher disposition towards mathematics which was caused by curiosity among 7 components of the mathematical disposition.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.