• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.77-84
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• 2021

This study attempted to find a way to maintain the quality of mixing baby wild leaf vegetables with existing baby leaf vegetables in various ratios. The crops for mixing baby leaf vegetables were Peucedanum japoincum Thunberg and Ligularia stenocephala, as wild vegetables, and red romaine, which is widely used in young leafy vegetables. The mixing ratio of red romaine and wild vegetables was red romaine 0: mantilla oil 5: L. stenocephala ratio 5 (R0: P5: L5), red romaine 3.3: P. japoincum 3.3: L. stenocephala ratio 3.3 (R3.3: P3.3: L3.3), red romaine 5: P. japoincum 2.5: L. stenocephala 2.5 (R5: P2.5: L2.5), red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1), red romaine 10: P. japoincum 0: L. stenocephala 0 (R10: P0: L0). All treatments were packaged in OTR (oxygen transmittance) 10,000 cc m-2·day-1·atm-1 film and stored for 27 days at 2℃/85% RH conditions. Fresh weight, carbon dioxide, oxygen, and ethylene concentrations of the baby leaf packages were examined approximately every 3 days, and visual quality, chlorophyll content, and chromaticity were examined on the 27th day of storage. The oxygen and carbon dioxide concentration in the packages were affected by the respiration rate of the crop. As the mixing ratio of lettuce, which had a low respiration rate, increased, the oxygen concentration in the packages was higher and the carbon dioxide concentration was lower. Oxygen concentration decreased significantly after 15 days, but was remained above 16%, and on the contrary, carbon dioxide concentration was kept at 1-4% until the 15th, and then gradually increased to 2-5% on the 27th day. The concentration of ethylene was maintained at 3-6 µL·L-1 until the end of storage (27th day). Visual quality score measured at the end of storage was slightly less than 3.0, which is the limit of marketability of all treatments. Although there was no significant difference, the chlorophyll content (SPAD) of red romaine and P. japoincum were most similar with an initial value in R8:P1:1 treatment, and L. stenocephala was higher value in R8:P1:L1 and R5:P2.5:L2.5 treatments at the end of storage. The leaf color (L∗, a∗, b∗, chroma) of the three crops at end of storage compared with the heat map showed the least change in the R5:P2.5:L2.5 and R8:P1:L1 treatments at the end of storage. Among them, R8:P1:L1 treatment maintained the highest chlorophyll content, the second lowest ethylene concentration, and adequate carbon dioxide concentration of 2-3%. Therefore, it is judged that the mixed ratio of red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1) is most suitable for the mixed package of baby leaf vegetables of these three crops.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.1-8
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• 2022

In order to manufacture feed suitable for consumer use and provide feed value information, we analyzed the feed components of the four main forage rice varieties by plant parts harvested 30 days after heading. The contents of the six feed ingredients were significantly different (p<0.05) among harvested parts. In the panicle, the crude protein (CP) (6.97%) and lignin (3.11%) were the highest, while the crude ash (CA) and neutral detergent fiber (NDF) contents were significantly lower, resulting in a total digestible nutrient (TDN) content of 77.29%, which is higher than that of the stem (64.82%) and leaf blade and sheath (LBS) (63.57%) (p<0.05). In contrast, the content of crude fat (CF) did not differ significantly among parts (p<0.05). In panicles from 'Jonong', 'Nokyang' and 'Yeongwoo', the TDN content of each cultivar was 78.48-79.07%, with no significant difference among the varieties. In 'Mogwoo' (Mw), the CP content was 8.70%, which was much higher than that of other varieties (p<0.05). In particular, the Mw TDN content was slightly lower in the panicle (72.95%) but higher in the stem (75.37%) and LBS (66.49%) than in the other varieties. The CA, NDF, acid detergent fiber (ADF), and lignin contents were also very low compared to other varieties; therefore, the feed value of the stem and LBS was excellent. In addition, the total dry matter weight (DMW) was 123 g per hill, which was much higher than 82-105 g per hill for other varieties. The distribution of DMW by part was LBS (56.9 g), stem (36.8 g), and panicle (29.3 g), and because the parts, except the panicles, were much higher than the 43-57% of other varieties (grain straw ratio: 76%), rice straw is advantageous in terms of quantity and feed value when used as forage on farms. The relative feed value (RFV) of the four cultivars ranged from 86.79-403.74 across all parts, and hay of grade 3 or higher with an RFV of 100 or more increased with delayed heading in both stems and LBS. This is due to the accumulation of starch into grains during ripening, which supports the observation that the RFV of the early flowering 'Jonong' and 'Nokyang' panicles increased.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.1
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• pp.26-42
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• 2022

The purpose of this study is to examine and understand the area and the original outer boundaries of Hamchunwon(含春苑), which was the outer royal garden of Gyeonghuigung Palace, which existed before the site of the Russian legation. The results of the study are as follows. First, examining the 3 types of drawings prepared for securing the Russian legation's site and constructing a new building, it was confirmed that two low peaks, which appear to be the original terrain of Hamchunwon, existed in the north and south directions inside the site. According to the initial plan of the of the legation's site, it appears that the entrance of the legation building is connected to the Saemunan-ro in the northwest. However, according to the report made at the time when the Russian temporary minister Veber purchased the legation's site, it was recorded that the site already had a narrow entrance and a dirt road in place, and hence, it was connected to Saemunan-ro. This fact makes it possible to learn that the line of movement for officials and the original gate were located to the northwest of the site planned as the entrance of the legation building towards Hamchunwon. Second, the site was created by cutting the top of the high hill at the time of the construction of the legation building, and as a result, a two tiered staircase typed terrace was built. The ground on which the main building and the secretary's building, etc., were erected was made by cutting the highest peak and solidifying it flat, and a large quantity of soil was used for grading. In the case of the northern area of the main building, the traces of leveling the terrain by cutting the mountains are apparent, and an observation typed garden with a walking path and pavilion was formed by utilizing the physical environment equipped with an easy view. This may be considered as a use which is consistent with the topographical conditions of creating an outer royal garden to block the civilian views on a high terrain overlooking the palace. Third, Hamchunwon's fences were partially exposed in the photos from the 1880s through the 1890s, which demonstrate the spatial changes made around the US, UK, and the Russian legations. As a result of the photo analysis performed, Hamchunwon occupies the northern area of the Russian legation's site, and it is estimated that the north, west, and east walls of the legation resembled those of Hamchunwon. The area to the south of the Russian legation was originally a place made available for civilian houses, and it was possible to examine the circumstances of purchasing dozens of civilian houses and farmlands according to various materials. Fourth, Hamchunwon, which was formed as the outer royal garden of Gyeongdeokgung Palace of Lord Gwanghaegun, lost its sense of place as an outer royal garden when the entire building of Gyeonghuigung Palace was torn down and used as a construction members during the reconstruction of Gyeongbokgung Palace, and faded away as the site was sold to Russia around 1885. The area where Hamchunwon used to be located transformed into a core space of the Russian legation where the main building and garden were located after the construction of the new building. Hence, Hamchunwon, which was limited to the northern area of the Russian legation, does not carry the temporal and spatial context with Gyeongungung Palace and Seonwonjeon which were constructed after 1897, and it is determined that the view of Seonwonjeon as Baehoorim or Baegyeongrim is not valid.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.133-144
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• 2022

This study was conducted to investigate the cultivation period, adaptive genetic resources, growth and development patterns, and water consumption for rice cultivation in the desert environment of United Arab Emirates (UAE). R esearch on rice cultivation in the desert environment is expected to contribute to resolving food shortages caused by climate change and water scarcity. It was found that the optimal cultivation period of rice was from late November to late April of the following year during which the low temperature occurred at the vegetative growth stage of rice in the UAE. Asemi and FL478 were selected to be candidate cultivars for temperature and day-length conditions in the desert areas as a result of pre-testing genetic resources under reclaimed soil and artificial meteorological conditions. In the desert environment in the UAE, FL478 died before harvest due to the etiolation and poor growth in the early stage of growth. In contrast, Asemi overcame the etiolation in the early stage of growth, which allowed for harvest. The vegetative growth phases of Asemi were from early December to early March of the following year whereas its reproductive growth and ripening phases were from early March to late March and from late March to late April, respectively. The yield of milled rice for Asemi was 763kg/10a in the UAE, which was about 41.8% higher than that in Korea. Such an outcome was likely due to the abundant solar radiation during the reproductive growth and grain filling periods. On the other hand, water consumption during the cultivation period in the UAE was 2,619 ton/10a, which was about three times higher than that in Korea. These results suggest that irrigation technology and development of cultivation methods would be needed to minimize water consumption, which would make it economically viable to grow rice in the UAE. In addition, select on of genetic resources for the UAE desert environments such as minimum etiolation in the early stages of growth would be merited further studies, which would promote stable rice cultivation in the arid conditions.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.377-388
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• 2022

Laboratory-scale experiments were performed to identify the As removal mechanism of the residual slag generated after the mineral carbonation process. The residual slags were manufactured from the steelmaking slag (blast oxygen furnace slag: BOF) through direct and indirect carbonation process. RDBOF (residual BOF after the direct carbonation) and RIBOF (residual BOF after the indirect carbonation) showed different physicochemical-structural characteristics compared with raw BOF such as chemical-mineralogical properties, the pH level of leachate and forming micropores on the surface of the slag. In batch experiment, 0.1 g of residual slag was added to 10 mL of As-solution (initial concentration: 203.6 mg/L) titrated at various pH levels. The RDBOF showed 99.3% of As removal efficiency at initial pH 1, while it sharply decreased with the increase of initial pH. As the initial pH of solution decreased, the dissolution of carbonate minerals covering the surface was accelerated, increasing the exposed area of Fe-oxide and promoting the adsorption of As-oxyanions on the RDBOF surface. Whereas, the As removal efficiency of RIBOF increased with the increase of initial pH levels, and it reached up to 70% at initial pH 10. Considering the PZC (point of zero charge) of the RIBOF (pH 4.5), it was hardly expected that the electrical adsorption of As-oxyanion on surface of the RIBOF at initial pH of 4-10. Nevertheless it was observed that As-oxyanion was linked to the Fe-oxide on the RIBOF surface by the cation bridge effect of divalent cations such as Ca²⁺, Mn²⁺, and Fe²⁺. The surface of RIBOF became stronger negatively charged, the cation bridge effect was more strictly enforced, and more As can be fixed on the RIBOF surface. However, the Ca-products start to precipitate on the surface at pH 10-11 or higher and they even prevent the surface adsorption of As-oxyanion by Fe-oxide. The TCLP test was performed to evaluate the stability of As fixed on the surface of the residual slag after the batch experiment. Results supported that RDBOF and RIBOF firmly fixed As over the wide pH levels, by considering their As desorption rate of less than 2%. From the results of this study, it was proved that both residual slags can be used as an eco-friendly and low-cost As remover with high As removal efficiency and high stability and they also overcome the pH increase in solution, which is the disadvantage of existing steelmaking slag as an As remover.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.39 no.1
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• pp.15-26
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• 1994

To understand the relative contribution of varietal and environmental variation on various grain quality components in rice, grain appearance, milling recovery, several physicochemical properties of rice grain and texture or palatability of cooked rice for milled rice materials of seven cultivars(five japonica & two Tongil-type), produced at six locations of the middle and southern plain area of Korea in 1989, were evaluated and analyzed the obtained data. Highly significant varietal variations were detected in all grain quality components of the rice materials and marked locational variations with about 14-54% portion of total variation were recognized in grain appearance, milling recovery, alkali digestibility, protein content, K /Mg ratio, gelatinization temperature, breakdown and setback viscosities. Variations of variety x location interaction were especially large in overall palatability score of cooked rice and consistency or set- back viscosities of amylograph. Tongil-type cultivars showed poor marketing quality, lower milling recovery, slightly lower alkali digestibility and amylose content, a little higher protein content and K /Mg ratio, relatively higher peak, breakdown and consistency viscosities, significantly lower setback viscosity, and more undesirable palatability of cooked rice compared with japonica rices. The japonica rice varieties possessing good palatability of cooked rice were slightly low in protein content and a little high in K /Mg ratio and stickiness /hardness ratio of cooked rice. Rice 1000-kernel weight was significantly heavier in rice materials produced in Iri lowland compared with other locations. Milling recovery from rough to brown rice and ripening quality were lowest in Milyang late-planted rice while highest in Iri lowland and Gyehwa reclaimed-land rice. Amylose content of milled rice was about 1% lower in Gyehwa rice compared with other locations. Protein content of polished rice was about 1% lower in rice materials of middle plain area than those of southern plain regions. K/Mg ratio of milled rice was lowest in Iri rice while highest in Milyang rice. Alkali digestibility was highest in Milyang rice while lowest in Honam plain rice, but the temperature of gelatinization initiation of rice flour in amylograph was lowest in Suwon and Iri rices while highest in Milyang rice. Breakdown viscosity was lowest in Milyang rice and next lower in Ichon lowland rice while highest in Gyehwa and Iri rices, and setback viscosity was the contrary tendency. The stickiness/hardness ratio of cooked rice was slightly lower in southern-plain rices than in middle-plain ones, and the palatability of cooked rice was best in Namyang reclaimed-land rice and next better with the order of Suwon$\geq$Iri$\geq$Ichon$\geq$Gyehwa$\geq$Milyang rices. The rice materials can be classified genotypically into two ecotypes of japonica and Tongil-type rice groups, and environmentally into three regions of Milyang, middle and Honam lowland by the distribution on the plane of 1st and 2nd principal components contracted from eleven grain quality properties closely associated with palatability of cooked rice by principal component analysis.

• The Mathematical Education
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• v.62 no.3
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• pp.381-400
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• 2023

As the era of solving various and complex problems in the real world using artificial intelligence and big data appears, problem-solving competencies that can solve realistic problems through a mathematical approach are required. In fact, the 2015 revised mathematics curriculum and the 2022 revised mathematics curriculum emphasize mathematical modeling as an activity and competency to solve real-world problems. However, the real-world problems presented in domestic and international textbooks have a high proportion of artificial problems that rarely occur in real-world. Accordingly, domestic and international countries are paying attention to the reality of mathematical modeling tasks and suggesting the need for authentic tasks that reflect students' daily lives. However, not only did previous studies focus on theoretical proposals for reality, but studies analyzing teachers' perceptions of reality and their competency to reflect reality in the task are insufficient. Accordingly, this study aims to analyze in-service mathematics teachers' perception of reality among the characteristics of tasks for mathematical modeling and the in-service mathematics teachers' competency for designing the mathematical modeling tasks. First of all, five criteria for satisfying the reality were established by analyzing literatures. Afterward, teacher training was conducted under the theme of mathematical modeling. Pre- and post-surveys for 41 in-service mathematics teachers who participated in the teacher training was conducted to confirm changes in perception of reality. The pre- and post- surveys provided a task that did not reflect reality, and in-service mathematics teachers determined whether the task given in surveys reflected reality and selected one reason for the judgment among five criteria for reality. Afterwards, frequency analysis was conducted by coding the results of the survey answered by in-service mathematics teachers in the pre- and post- survey, and frequencies were compared to confirm in-service mathematics teachers' perception changes on reality. In addition, the mathematical modeling tasks designed by in-service teachers were evaluated with the criteria for reality to confirm the teachers' competency for designing mathematical modeling tasks reflecting the reality. As a result, it was shown that in-service mathematics teachers changed from insufficient perception that only considers fragmentary criterion for reality to perceptions that consider all the five criteria of reality. In particular, as a result of analyzing the basis for judgment among in-service mathematics teachers whose judgment on reality was reversed in the pre- and post-survey, changes in the perception of in-service mathematics teachers was confirmed, who did not consider certain criteria as a criterion for reality in the pre-survey, but considered them as a criterion for reality in the post-survey. In addition, as a result of evaluating the tasks designed by in-service mathematics teachers for mathematical modeling, in-service mathematics teachers showed the competency to reflect reality in their tasks. However, among the five criteria for reality, the criterion for "situations that can occur in students' daily lives," "need to solve the task," and "require conclusions in a real-world situation" were relatively less reflected. In addition, it was found that the proportion of teachers with low task development competencies was higher in the teacher group who could not make the right judgment than in the teacher group who could make the right judgment on the reality of the task. Based on the results of these studies, this study provides implications for teacher education to enable mathematics teachers to apply mathematical modeling lesson in their classes.