• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.93-105
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• 2022

This study investigated the aspects of how three preservice elementary school teachers conducted the data-text transforming discourses in their science simulation teaching and how their epistemological conversations worked for learners' construction of scientific knowledge. Three preservice teachers, who had presented simulation teaching on the seasonal change of constellations, participated in the study. The results revealed that one preservice teacher, who had implemented the transforming discourses of data-to-evidence and model-to-explanation, appeared to facilitate learners' knowledge construction. The other two preservice teachers had difficulty helping learners construct science knowledge due to their lack of transforming discourses. What we should consider for improving preservice elementary school teachers' teaching competencies was discussed based on a detailed comparison of three cases of preservice teachers' data-text transforming.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.1
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• pp.33-47
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• 2021

This study developed a big data-based STEAM (Science, Technology, Engineering, Art & Mathematics) program using WWT (World Wide Telescope), focusing on content elements of 'solar system', 'star and universe' in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to one middle school 176 students simple random sampled. The results of this study are as follows. First, we developed a program to encourage students to actively and voluntarily participating, utilizing the astronomical data platform WWT. Second, in the paired t-test based on the difference between the pre- and post-scores of the creative problem solving measurement test, significant statistical test results were shown in 'idea adaptation', 'imaging', 'analogy', 'idea production' and 'elaboration' sub-factors except 'attention task' sub-factor (p < .05). Third, in the paired t-test based on the difference between the pre- and post-scores of the STEAM attitude test, significant statistical test results were shown in 'interest', 'communication', 'self-concept', 'self-efficacy' and 'science and engineering career choice' sub-factors except 'consideration' and 'usefulness / value recognition' sub-factors (p < .05). Fourth, in the STEAM satisfaction test conducted after class application, the average values of sub-factors were 3.16~3.90. The results indicated that students' understanding and interest in the science subject improved significantly through the big data-based STEAM program using the WWT.

• Journal of Science Education
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• v.44 no.2
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• pp.205-213
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• 2020

The purpose of this study is to investigate the effects of elementary school students' knowledge and information processing competence and collaborative problem-solving ability in scientific inquiry class using data measured with digital inquiry tools. To this end, three classes of 5th grade elementary schools in S-city, Gyeongnam were selected as experimental groups and three classes as control groups. The control group conducted traditional lecture-style classes, and the experimental group conducted scientific inquiry classes using scientific data. The following results were obtained through questionnaires after class. First, science inquiry classes using scientific data helped elementary school students improve their knowledge and information processing competence. Second, scientific inquiry classes using scientific data improved elementary school students' cooperative problem-solving ability. From the above results, it was found that scientific inquiry classes using scientific data are needed to improve the knowledge information processing competence and cooperative problem solving ability of elementary school students. Based on this research, it is necessary to study a specific teaching and learning environment that can activate scientific inquiry class using data measured with digital inquiry tools in the future.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.303-323
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• 2023

The purpose of this study is to introduce this language model in the era of generative AI such as ChatGPT, and to provide direction for data literacy education components in libraries using it. To this end, the following three research questions are proposed. First, the technical features of ChatGPT-like language models are examined, and then, it is argued that data literacy education is necessary for the proper and accurate use of information by users using a service platform based on generative AI technology. Finally, for library data literacy education in the ChatGPT era, it is proposed a data literacy education scheme including seven components such as data understanding, data generation, data collection, data verification, data management, data use and sharing, and data ethics. In conclusion, since generative AI technologies such as ChatGPT are expected to have a significant impact on users' information utilization, libraries should think about the advantages, disadvantages, and problems of these technologies first, and use them as a basis for further improving library information services.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.330-343
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• 2020

In this study, STEAM program on the subject of 'atmospheric circulation and surface current' was produced based on data and applied to 106 first-year high school students to analyze its effect and problem-solving processes. This program was organized to collect, refine, visualize, and analyze data and to allow communication processes to proceed based on these results. Using this, the concept of circulation in daily life was expanded from a global perspective to identify problems about circulation around the world. As a result of the application of the program, significant changes were identified in knowledge information processing competency. Also, significant changes were made in terms of convergence and creativity, which are sub categories among STEAM core competencies. It also sought to obtain suggestions for data-based STEAM education by analyzing students' responses in the form of a Text network.

• Journal of The Korean Association of Information Education
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• v.22 no.6
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• pp.639-649
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• 2018

Physical computing, a concrete operational activity using tools, can help children to understand principles of computer science by observing interactions between a computer and the real world directly. This study aims to design and develop an android application to utilize students' smart phones as a tool of physical computing education. The application includes one class designed to collect data using android smart phone sensors and another designed to transfer the data to a computer which students can use to learn programming. The physical computing application can be used to help students understand the principles of computer science more easily in schools not equipped with physical computing tools.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• The Journal of Korean Association of Computer Education
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• v.23 no.3
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• pp.49-58
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• 2020

Organizing entrance evaluation system for selecting students who can become expert in data science field according to need of the age and social demand is important. This study was conducted for the purpose of analyzing data science field graduate school entrance evaluation system and deriving implications after taking into account the importance of talents possessing convergence competency. For this aim, a total of 22 graduate schools in 7 countries have been selected targeting data scientist postgraduate program around the world. The selected graduate schools have been analyzed based on qualifications, necessary skills prior to entrance, entrance conditions, and selection methods. As a result of the analysis, 'graduate school which I can apply for regardless of possessing undergraduate degree or undergraduate major (63.6 percent)' in qualifications category, 'graduate school which mentioned skills required in completing master's degree prior to entrance (63.6 percent)' in skills required prior to entrance category, 'graduate school which does not mention separate entrance condition (81.8 percent)' in entrance conditions category, and 'graduate school selecting students merely based on document screening (68.2 percent)' in selection methods category took the highest portion. Based on the above, this study summarized the results of the data scientist process and suggested implications for objectifying admission evaluation.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.65-73
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• 2014

The purpose of this study is to examine how time management, usefulness, and interest predict m-learning outcomes. For this study, 144 high school students participated in m-learning activities during science classes. After 5 week of classes, they responded the following surveys: time management, usefulness, interest, satisfaction, perceived achievement and learning persistence. Multiple regression analyses with correlation applied to this study as a data analysis method. The results showed that time management, usefulness, interest significantly predicted learning satisfaction and persistence. In addition, time management and usefulness significantly predicted perceived achievement, Therefore, these findings imply that time management, usefulness should be considered for designing m-learning activities in high school science class.

• 한국정보교육학회:학술대회논문집
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• 2007.01a
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• pp.295-300
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• 2007

지난 20년간의 지속적인 투자와 장기간에 걸친 교육을 통해 육성된 우수한 인재 때문에 우리나라는 세계적인 IT강국이 되었다고 할 수 있다. 그러나 현 컴퓨터교육은 컴퓨터과학의 원리에 대한 체계적인 학습을 통해 창의적인 문제해결력과 논리적 사고력을 신장시키는 교육이 아니라 단순히 응용소프트웨어 및 학습용 소프트웨어의 기능을 익히는 형태로 이루어지고 있다. 이에 본 연구에서는 초등학생을 대상으로 지식교육의 중심이라 할 수 있는 컴퓨터 교과 내용학의 한분야인 컴퓨터그래픽 중 비트맵 데이터 표현에 관한 교수-학습 방법을 설계하였다.