• Journal of The Korean Association For Science Education
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• v.24 no.6
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• pp.1158-1172
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• 2004

This study searched secondary school students' epistemological views and ontological views about nature and the root causes of such their views. The subjects were 156 secondary school students and data were gathered by the questionnaire developed based on preceding researches. As a result, many secondary school students had epistemological views of unknowable nature. There were various root causes of their epistemological views such as regularity and harmony of nature, predictable and circular natural phenomenon, causation, the relation between human and nature. On the other hand, a lot of secondary school students had ontological view of supernatural nature. Their religious beliefs were very powerful influence their supernatural ontological views. The nature is the object of science and the physical world. Because those views supply science educators basic backgrounds how leaners understand science class, secondary school students' epistemological views and ontological views are precious information. From now on, it is necessary to study relations between students' epistemological views and ontological views and their science class processes.

• School Mathematics
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• v.18 no.4
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• pp.773-791
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• 2016

Teacher noticing ability has been considered as one of important elements influencing a quality of teaching. Noticing is closely related to teachers' in the moment decision making in a class, and teachers notice things as they create and interact with their classroom setting. Mathematics teachers as an expert should notice students' mathematics learning during a class. The aim of this study was to analyze how mathematics teacher's pre-noticing activity that the teacher anticipated students' typical strategies and difficulties in learning targeted mathematics knowledge and prepared appropriate responses worked in practice. As a result, the teacher conducted three types of noticing in her classes: noticing shaping students' understanding by using students' misconceptions or errors; noticing creating students' learning opportunities based on their prior knowledge; noticing improving students' informal reasoning. This study concluded with discussion about the positive effect of teacher's pre-noticing activity on her actual noticing in practice, as well as implications for teacher education.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.267-277
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• 2022

Achievement at university is recognized in a comprehensive sense as the level of qualitative change and development that students have embodied as a result of their experience in university education. Therefore, the academic achievement of university students will be given meaning in cooperation with the historical and social demands for diverse human resources such as creativity, leadership, and global ability, but it is practically an indicator of the outcome of university education. Measurement of academic achievement by such credits involves many problems, but in particular, standardization of academic achievement by credits based on evaluation methods, contents, and university rankings is a very difficult problem. In this study, we present a model that uses machine learning techniques to predict whether or not academic achievement is excellent for D-University graduates. The variables used were analyzed using up to 96 personal information and bachelor's information such as graduation year, department number, department name, etc., but when establishing a future education course, only the data after enrollment works effectively. Therefore, the items to be analyzed are limited to the recommended ability to improve the academic achievement of the department/student. In this research, we implemented an academic achievement prediction model through analysis of core abilities that reflect the philosophy, goals, human resources image, and utilized machine learning to affect the impact of the introduction of the prediction model on academic achievement. We plan to apply the results of future research to the establishment of curriculum and student guidance conducted in the department to establish a basis for improving academic achievement.

• The Journal of Sustainable Design and Educational Environment Research
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• v.20 no.2
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• pp.1-10
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• 2021

The results of research and analysis are as follows: First, there were 108 types of change patterns in the number of elementary school students. Second, based on the similarity of 108 types of change patterns in the number of students, a type system diagram was presented. Third, in the case of a total of 18 types of change patterns in the number of students, the number of students decreased significantly due to the establishment of additional schools on top of the existing schools. The result of a a long-term survey and analysis on the trend of increase and decrease in the number of students across the country shows an urgent need todevelop policy tasks across the entire school accommodation plan, such as the establishment of differentiated schools suitable for regional characteristics, relocation, consolidation, reorganization of school districts, remodeling, and appropriatization projects.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.265-274
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• 2017

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.487-492
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• 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 The Korean Association of Information Education
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• v.17 no.2
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• pp.115-123
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• 2013

With the rapid growth of mobile technologies, the mobile learning has been gradually considered as a efficient and effective learning form because it breaks the limitations of learning time and space occurring in the traditional classroom learning. Therefore, this research aims how the learners' m-learning efficacy, ubiquity, perceived usefulness, and ease of use predict perceived learning achievement and satisfaction Participants were 144 11th-grade students in A high school in Kyungnam area, Korea. After studying science class using mobile devices, they responded the following surveys: m-learning efficacy, ubiquity, perceived usefulness, ease of use, and satisfaction. Multiple regression analyses with correlation were applied to this study as a data analysis method. Findings of this study include: (a) m-learning efficacy and perceived usefulness predicted learning satisfaction, (b) perceived usefulness and ubiquity predicted perceived learning achievement. These findings imply that m-learning efficacy, perceived usefulness, ubiquity should be valued to enhance learning outcomes in mobile learning class.

• Journal of The Korean Association of Information Education
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• v.20 no.5
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• pp.475-486
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• 2016

As software transforms the structure of industry, it becomes a key measure in determining market competitiveness. Therefore, various educational efforts have been attempted in Korea to cultivate software professionals to secure software competitiveness. While previous studies had focused mainly on the cognitive effectiveness of software education, the authors tried to focus on affective perspectives. The authors, therefore, aimed to analyze the predictive power of the recognition of software importance and learning flow on affective outcomes, such as efficacy of computational thinking skills, and attitude toward, and satisfaction with, software education. The data were collected from 103 sixth grade students who participated in a software education. Results show that software importance and learning flow had significant predictive power on affective outcomes; Learning flow mediated the relationship between software importance and affective outcomes. This study provides practical implications for improving affective outcomes in the design and implementation of software education.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.2
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• pp.183-195
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• 2011

This study was conducted to investigate dietary behavior patterns of high school students. As for regularity of meal, female students were significantly more regular than those of the male students in a day. They answered 'between 4th and 6th grades in elementary school' as 'the time for formation about number of meal intake'. As for a highly influential meal, males were higher answered 'lunch (41.0%)' while females were higher answered 'breakfast (39.8%)'. About 'number of meals per day by the grade', they ate 3 times per day mostly. As for the time for formation about number of meal intake: 'before 4th to 6th in elementary school'> 'before elementary school'. In the result of regularity of meal and general characteristics, a family of 5 was higher in regularity and those with pocket money showed lower regularity in meal. As for the person who prepares a meal, mothers prepared meals regularly. Also, higher parents age and education level resulted in more regularity in meals. In number of meals per day and general characteristics, they were eating 3 times; moreover, this trend was evident as parents' age and education level and the household income was higher. Students answered generally regularity in meal in family where parents' jobs were administrative assistant (father job (56.9%)) and housewife (mother (56.9%). In the formation time of meal intake number and general characteristics, they answered order 'middle school'> 'before elementary school'. A highly influential meal, they answered as the highest 'lunch' (37.6%). This study may provide information on dietary behavior of high school students, suggesting that nutrition education or counseling can improve food habits and develop positive behavior.