• Communications of Mathematical Education
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• v.20 no.1 s.25
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• pp.33-59
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• 2006

This study is intended to reconsider the meaning of the education for gifted/talented children, the foundation object of science high school by examining the behavior characteristics between gifted students and talented students in open-end mathematical problem solving and to provide the basis for realization of 'meaningful teaming' tailored to the learner's level, the essential of school education. For the study, 8 students (4 gifted students and 4 talented students) were selected out of the 1 st grade students in science high school through the distinction procedure of 3 steps and the behavior characteristics between these two groups were analyzed according to the basis established through the literature survey. As the results of this study, the following were founded. (1) It must be recognized that the constituent members of science high school were not the same excellent group and divided into the two groups, gifted students who showed excellence in overall field of mathematical behavior characteristics and talented students who had excellence in learning ability of mathematics. (2) The behavior characteristics between gifted students and talented students, members of science high school is understood and a curriculum of science high school must include a lesson for improving the creativity as the educational institutions for gifted/talented students, unlike general high school. Based on these results, it is necessary to try to find a support plan that it reduces the case which gifted students are generalized with common talented students by the same curriculum and induces the meaningful loaming to learners, the essential of school education.

• Communications of Mathematical Education
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• v.35 no.1
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• pp.97-118
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• 2021

Since the characteristics of teachers that affect mathematics academic achievement are constantly changing and affecting mathematics achievement, longitudinal studies that can predict and analyze growth are needed. This study used data from middle and high school students from 2013(first year of middle school) to 2017(second year of high school) of the Seoul Education Longitudibal Study(SELS). By classifying the longitudinal changes in mathematics academic achievement into similar subgroups, the direct influence of teachers' characteristics(professionalism, expectations, academic feedback) perceived by students on the longitudinal changes in mathematics academic achievement was examined. As a result of the study, it was found that the characteristics of mathematics teachers(professional performance, expectation, and academic feedback) in group 1(343 students), which included the top 14.5% of students, did not directly affect longitudinal changes in mathematics academic achievement. Students in the middle 2nd group(745, 32.2%) had academic feedback from the mathematics teacher, and the 2nd group(1225 students) in the lower 53%, which included most of the students, showed that the expectations of the mathematics teacher were the longitudinal mathematics achievement. The change has been shown to have a direct effect. This suggests that support for teaching and learning should also reflect this, as the direct influence of teachers' professionalism, expectations, and academic feedback on longitudinal changes in mathematics academic achievement is different according to the characteristics and dispositions of students.

• The Journal of Korean Society for School & Community Health Education
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• v.22 no.4
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• pp.49-60
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• 2021

Objectives: Laboratories have various latent physical, chemical, biological, and ergonomical factors according to the diversification and fusion of research and development activities. This study aims to investigate the chemical exposure concentrations of college laboratories and evaluate their health risks, and use them as basic data to promote the health of college students. Methods: The sampling and analysis of harmful chemicals in the air in laboratories were performed using Method 1500 of the U.S. National Institute for Occupational Safety and Health (NIOSH)의 Method 1500. The harmful chemicals in the laboratories were divided into carcinogenic and non-carcinogenic chemicals. Risk assessment was performed using the cancer risk (CR) for carcinogenic chemicals and using the hazard index (HI) for non-carcinogenic chemicals. Results: The harmful chemicals in college laboratories consisted of acetone, diethyl ether, methylene chloride, n-hexane, ethyl acetate, chloroform, tetrahydrofuran, toluene, and xylenes. They showed the highest concentrations in laboratories A (acetone 0.001~2.34ppm), B (chloroform 0.95~6.35ppm), C (diethyl ether 0.08~8.68ppm), and D (acetone 0.07~14.96ppm). The risk assessment result for non-carcinogenic chemicals showed that the HI of methylene chloride was 2.052 for men and 2.333 for women, the HI of N-hexane was 4.442 for men and 5.05 for women. Thus, the HI values were higher than 1. The risk of carcinogenic chemicals is determined by an excess cancer risk (ECR) value of 1.0×10^-5, which means that one in 100,000 people has a cancer risk. The ECRs of chloroform exceeded 1.0×10^-5 for both men and women, indicating the possibility of cancer risk. Conclusion: College laboratories showed the possibility of non-carcinogenic health risks for methylene chloride, n-hexane, tetrahydrofuran (THF), toluene, and xylenes, and carcinogenic health risks for chloroform, methylene chloride. However, this study used the maximum values of measurements to determine the worst case, and assumed that the subjects were exposed to the corresponding concentrations continuously for 8 hours per day for 300 days per year. In consideration of the nature of laboratory environment in which people are intermittently exposed, rather than continuously, to the chemicals, the results of this study has an element of overestimation.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.159-181
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• 2023

The purpose of this study is to explore secondary mathematics teachers' perceptions on Artificial Intelligence (AI). For this purpose, we conducted three focus group interviews with 18 secondary in-service mathematics teachers and analyzed their perceptions on AI for math and math for AI. The secondary in-service mathematics teachers perceive that AI allows to implement different types of mathematics instruction but has limitations in exploring students' mathematical thinking and having emotional interactions with students. They also perceive that AI makes it easy to develop assessment items for teachers but teachers' interventions are needed for grading essay-type assessment items. Lastly, the secondary in-service mathematics teachers agree the rationale of adopting the subject ＜Artificial Intelligence Mathematics＞ and its needs for students, but they perceive that they are not well prepared yet to teach the subject and do not have sufficient resources for teaching the subject and assessing students' understanding about the subject. The findings provide implications and insights for developing individualized AI learning tools for students in the secondary level, providing AI assessment tools for teachers, and offering professional development programs for teachers to increase their understanding about the subject.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.569-590
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• 2023

The purpose of this case study is to compare and analyze the covariational reasoning levels of two middle school students revealed in the process of solving and generalizing algebra word problems. A class was conducted with two middle school students who had not learned quadratic equations in school mathematics. During the retrospective analysis after the class was over, a noticeable difference between the two students was revealed in solving algebra word problems, including situations where speed changes. Accordingly, this study compared and analyzed the level of covariational reasoning revealed in the process of solving or generalizing algebra word problems including situations where speed is constant or changing, based on the theoretical framework proposed by Thompson & Carlson(2017). As a result, this study confirmed that students' covariational reasoning levels may be different even if the problem-solving methods and results of algebra word problems are similar, and the similarity of problem-solving revealed in the process of solving and generalizing algebra word problems was analyzed from a covariation perspective. This study suggests that in the teaching and learning algebra word problems, rather than focusing on finding solutions by quickly converting problem situations into equations, activities of finding changing quantities and representing the relationships between them in various ways.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.318-327
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• 2007

The purpose of this study is to examine the effects of small-scale chemistry(SSC) laboratory activities implemented in high school chemistry II classes on the students' inquiry process skills and science-related attitudes. For this study, 112 students in the 12th grade were chosen and divided into an experimental and a control group. Seven SSC lab programs that can replace the traditional experiments in chemistry II textbooks were selected and administered to the experimental group while the traditional textbook experiments were administered to the control group. The results showed that there was a significant difference in the enhancement of inquiry process skills between the two groups while no significant difference was found in science-related attitudes. Further analysis showed that the difference in the inquiry process skills came from the basic inquiry process skills. The experimental group students thought that the SSC experiments have many advantages compared to the traditional experiments, e.g., individual work, learning lab and theory in parallel, short experiment time, safety, environmental aspects, etc. These results suggest that the SSC lab programs are valuable in high school chemistry classes and developing and distributing various SSC lab programs is needed to replace the traditional experiments in the current textbooks.