• Journal of Science Education
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• v.44 no.1
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• pp.92-111
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• 2020

The 2015 revised science curriculum and NGSS (Next Generation Science Standard) suggest computational thinking as an inquiry skill or competency. Particularly, concern in computational thinking has increased since the Ministry of Education has required software education since 2014. However, there is still insufficient discussion on how to integrate computational thinking in science education. Therefore, this study aims to prepare a way to integrate computational thinking elements into scientific inquiry by analyzing the related literature. In order to achieve this goal, we summarized various definitions of the elements of computational thinking and analyzed general problem solving process and scientific inquiry process to develop and suggest the model. We also considered integrated problem solving cases from the computer science field and summarized the elements of the Computational Thinking-Scientific Inquiry (CT-SI) model. We asked scientists to explain their research process based on the elements. Based on these explanations from the scientists, we developed 'Problem-finding' CT-SI model and 'Problem solving' CT-SI model. These two models were reviewed by scientists. 'Problem-finding' model is relevant for selecting information and analyzing problems in the theoretical research. 'Problem solving' is suitable for engineering problem solving process using a general research process and engineering design. In addition, two teachers evaluated whether these models could be used in the secondary school curriculum. The models we developed in this study linked with the scientific inquiry and this will help enhance the practices of 'collecting, analyzing and interpreting data,' 'use of mathematical thinking and computer' suggested in the 2015 revised curriculum.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Hwankyungkyoyuk
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• v.21 no.1
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• pp.82-96
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• 2008

The primary purpose of this research is to examine the career development of under-graduate environmental education program students. More specifically, it intends to understand how they perceive their career prospect, how they prepare for they career and what kind of career development assistance they receive from their school. For most of college students, a primary purpose for attending college is to prepare for a career, but often academic preparation in college is not enough to get the job they aspire. Career preparation becomes even more complex when their major does not dictate any specific career path or when the major does dictate a specific career path but it does not provide sufficient and quality job opportunities. Undergraduate environmental education programs are typical examples of the latter. To illuminate the understanding of the context surrounding undergraduate environmental education program students, semi-structured interviews were conducted through an instance message software program from August 17th, 2007 to August 20th, 2007. The subjects of this study consisted of 4 undergraduate students and 2 graduates of Hangu University (a tentative name). Each participant was interviewed once or twice for about 2 hours each interview. The findings of the study are as follow. First, they maintain a positive attitude regarding the career outlook and the career decision but it is not well founded. Second, career search and career preparation are conducted individually, and their school does not provide much systematic career assistance. Third, they did not see the relevance of the curriculum of their program to their future career. A few preliminary implications for practices related to career development may be drawn from the findings reported in this study. First, undergraduate students in environmental education programs appear to be in need of comprehensive career development assistance ranging from exploration of job opportunities related to majors, to development of job-seeking skills, to general issues of career preparation and choice. Experiential modes of career exploration seem to be helpful in clarifying their interest and aptitude in the area of environmental education. Second, career development assistance for these students should start at early college years. Third, these students may need help in exploring diverse opportunities related to their major as well as other academic areas. Finally, it is recommended to create more careers in the area of environmental education.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.509-520
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• 2015

College Scholastic Ability Test(CSAT) is a primary test to evaluate the study achievement of high-school students and used by most universities for admission decision in South Korea. Because its level of difficulty is a significant issue to both students and universities, the government makes a huge effort to have a consistent difficulty level every year. However, the actual levels of difficulty have significantly fluctuated, which causes many problems with university admission. In this paper, we build two types of data-driven prediction models to predict correct answer rate and to identify significant factors for CSAT English test through accumulated test data of CSAT, unlike traditional methods depending on experts' judgments. Initially, we derive candidate question-specific factors that can influence the correct answer rate, such as the position, EBS-relation, readability, from the annual CSAT practices and CSAT for 10 years. In addition, we drive context-specific factors by employing topic modeling which identify the underlying topics over the text. Then, the correct answer rate is predicted by multiple linear regression and level of difficulty is predicted by classification tree. The experimental results show that 90% of accuracy can be achieved by the level of difficulty (difficult/easy) classification model, whereas the error rate for correct answer rate is below 16%. Points and problem category are found to be critical to predict the correct answer rate. In addition, the correct answer rate is also influenced by some of the topics discovered by topic modeling. Based on our study, it will be possible to predict the range of expected correct answer rate for both question-level and entire test-level, which will help CSAT examiners to control the level of difficulties.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.88-95
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• 2002

In recent years, the security of hardware and software systems is one of the most essential factor of our safe network community. As elliptic Curve Cryptosystems proposed by N. Koblitz and V. Miller independently in 1985, require fewer bits for the same security as the existing cryptosystems, for example RSA, there is a net reduction in cost size, and time. In this thesis, we propose an efficient hardware architecture of underlying field arithmetic processor for Elliptic Curve Cryptosystems, and a very useful method for implementing the architecture, especially multiplicative inverse operator over GF$GF (2^m)$ onto FPGA and futhermore VLSI, where the method is based on optimized unit operation components. We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed and inversion speed has been improved 150 times, 480 times respectively compared with the thesis presented by Sarwono Sutikno et al. [7]. The designed underlying arithmetic processor can be also applied for implementing other crypto-processor and various finite field applications.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.464-477
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• 2013

There is a great need to develop a training program proven to change behavior and improve knowledge. The purpose of this study was to evaluate employee hygiene knowledge, hygiene practice, and cleanliness, before and after HACCP system implementation at one small-size restaurant. The efficiency of the system was analyzed using time-temperature control after implementation of u-HACCP$^{(R)}$. The employee hygiene knowledge and practices showed a significant improvement (p<0.05) after HACCP system implementation. In non-heating processes, such as seasoned lettuce, controlling the sanitation of the cooking facility and the chlorination of raw ingredients were identified as the significant CCP. Sanitizing was an important CCP because total bacteria were reduced 2~4 log CFU/g after implementation of HACCP. In bean sprouts, microbial levels decreased from 4.20 logCFU/g to 3.26 logCFU/g. There were significant correlations between hygiene knowledge, practice, and microbiological contamination. First, personnel hygiene had a significant correlation with 'total food hygiene knowledge' scores (p<0.05). Second, total food hygiene practice scores had a significant correlation (p<0.05) with improved microbiological qualities of lettuce salad. Third, concerning the assessment of microbiological quality after 1 month, there were significant (p<0.05) improvements in times of heating, and the washing and division process. On the other hand, after 2 months, microbiological was maintained, although only two categories (division process and kitchen floor) were improved. This study also investigated time-temperature control by using ubiquitous sensor networks (USN) consisting of an ubi reader (CCP thermometer), an ubi manager (tablet PC), and application software (HACCP monitoring system). The result of the temperature control before and after USN showed better thermal management (accuracy, efficiency, consistency of time control). Based on the results, strict time-temperature control could be an effective method to prevent foodborne illness.