• Journal of Digital Convergence
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• v.17 no.8
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• pp.15-26
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• 2019

This study was to explore the domestic research trends, and education and operation cases concerned with Korean colleges in the fourth industrial revolution era. It was conducted through the analysis of 114 academic papers registered to the Korea Research Foundation, the newspaper articles, and the main 4-year university homepage from 2016 to April 2019. The results was as follows. Research papers have been increasing since 2016; research were conducted by humanities and social sciences as well as engineering academics interesting in research topics such as technologies, curriculum, and teaching and learning by mainly using quantitative research, literature research. As for the college education, reorganization of the undergraduate and majors centered on the science and engineering field, teaching and learning related with learner's participation and performance, and provide efficient academic affairs management and career guidance using Chatbot or Cloud computing. Industry-academia cooperation was focuses on the field of science and engineering. In future research, it is necessary to explore the research on college students' career and employment, the research on academic affairs management and infrastructure, the relational research considering the variables among college students and faculties, and the qualitative and mixed method approach.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.201-212
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• 2021

Curriculum content interest has been studied in terms of teachers rather than students in ways that help revise curriculum and develop textbooks. In this study, 227 first-year high school students were interested in what they learned in middle school and what they learned in high school, focusing on meaningful assumptions and contents for middle school and high school students. We analyze this difference in interest by gender and achievement level. According to the research results, first-year high school students, who are in the middle and high school transition period, have relatively high interest in themselves, such as youth development, compared to family needs or interest in family life. There were no gender differences, but there were differences in content interest depending on the level of achievement. This means that the content interest should be interpreted by considering various variables such as the development of learners, the situation of learners in the school-level transition period, and the entrance examination of universities. The results of this study will provide implications for future curriculum revisions, textbook development, and curriculum considerations, and will need to be carefully analyzed and utilized based on a learner-centered perspective.

• The Mathematical Education
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• v.62 no.4
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• pp.491-513
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• 2023

High achievement in mathematics is a very complex process in which various factors such as cognitive factors, affective factors, and social and environmental factors work respectively and complementary. A number of previous studies conducted so far have shown that there are certain factors affecting math learning and these factors have positive or negative effects on it. However, these studies were conducted with limited variables and it was not possible to present a comprehensive analysis of what would be necessary to get good achievements in mathematics learning. Therefore, in this study, we analyzed the process of experience of students who experienced success in mathematics learning using the analysis method of the grounded theory. In addition, the collected data was analyzed to explain the process of leading to the successful experience in mathematics learning. As a result of the analysis, it was revealed that students form their identity as successful learners through the processes of 'new phase stage', 'experience accumulation stage', 'stand-up stage', and 'maintenance effort stage'. Through this study, we were able to get implications for what actions are needed to experience success in math learning by looking at the process of the experience what interviewees have gone through.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.