• Communications of Mathematical Education
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• v.34 no.3
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• pp.299-324
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• 2020

The purpose of this study was to identify high school students' mathematics learning style and its characteristics according to their personality disposition types and to propose mathematics learning strategies fit into each personality disposition type. For this purpose, MBTI personality test and survey to find mathematics learning style for 375 high school students were executed. The results were as follows. First, many students highly evaluated the effects of private education and prefer reference book to textbook. Second, there were significant differences on following variable domains of mathematics learning style such as learning attitude, learning habit(concentrativeness to concept understanding), problem solving strategies(effort for problem comprehension, use of various strategies), self management(metacognition) by MBTI personality disposition types(SJ, SP, NT, NF groups). Third, based on the results, the following mathematics learning strategies fit into each personality disposition type were recommended. SJ type students are needed to effort creative approach for open problem and to use mindmap as mathematics learning strategy. SP type students are needed to fulfill stepwise problem solving process and to effort constantly practice long/short term learning objectives. NT type students are needed to expand opportunity to study with friends and to use SRN(self reflection note) or mathematics journal writings as mathematics learning strategy. NF type students are needed to use mathematics learning note writing activity which include logical basis for each step of problem solving and to invest more time on learning algebra which need meticulous calculation.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.35-44
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• 2021

As the execution speed of Python is slower than those of other programming languages (e.g., C, C++, and FORTRAN), Python is not considered to be efficient for writing numerical geodynamic code that requires numerous iterations. Recently, many computational techniques, such as the Just-In-Time (JIT) compiler, have been developed to enhance the calculation speed of Python. Here, we developed two-dimensional (2D) numerical geodynamic code that was optimized for the JIT compiler, based on Python. Our code simulates mantle convection by combining the Particle-In-Cell (PIC) scheme and the finite element method (FEM), which are both commonly used in geodynamic modeling. We benchmarked well-known mantle convection problems to evaluate the reliability of our code, which confirmed that the root mean square velocity and Nusselt number obtained from our numerical modeling were consistent with those of the mantle convection problems. The matrix assembly and PIC processes in our code, when run with the JIT compiler, successfully achieved a speed-up 30× and 258× faster than without the JIT compiler, respectively. Our Python-based FEM-PIC code shows the high potential of Python for geodynamic modeling cases that require complex computations.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.2
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• pp.209-217
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• 2003

Object：This study examined the diagnostic validity of the K-ABC and the K-LDES for identifying the cognitive deficits and the learning difficulty of children with learning disorder and to diagnose the learning disorder. Method：The clinical group consisted of 15 children with learning disorder or attention deficit hyperactivity disorder accompanying learning problem(LP) and 14 children with attention deficit hyperactivity disorder. They were diagnosed either learning disorder or attention deficit hyperactivity disorder based on DSM-IV criteria by child psychiatrists and clinical psychologists visiting Seoul National University Children’s Hospital. The normal group was composed of 15 children be going to an elementary school. All groups were between the age of 7 and 12. The K-ABC was administered to the clinical and the normal group. The K-LDES was also administered to mothers of all groups. Result：There were no significant differences on sequential, simultaneous, mental processing subscales of the K-ABC in three groups. However, The LP group showed slightly lower scores on Achievement scale and significant low scores on Reading/Decoding than the other groups. On K-LDES, LP group showed significant low scores on Listing, Thinking, Reading, Writing, Spelling, Mathematical calculation, Learning quotient(LQ) than the other groups. Also there were significant correlations between K-ABC and K-LDES subscales. Conclusion：The result of present study showed that the K-ABC and the K-LDES are a valid and effective instruments for evaluating and diagnose the learning disorder.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.