• Journal of Korea Game Society
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• v.15 no.3
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• pp.87-96
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• 2015

Fun and happiness are the intrinsic values of the human beings. Game is one of the most efficient tools which satisfies those intrinsic values. Approaches and methods combining game and education to provide the enjoyable education environments have been increasing. As use of gamification in corporate and school education environments for human resources is increasing, significant voices of concern on the limitations and side effects of combining game and education are continuously increasing. The purpose of this paper is to energize the use of gamification in education environments by clarifying the limitations and side effects of gamification and seeking ways to overcome those factors. This paper summarizes an observational research on the limitations and side effects of gamification in education environments, and provides the interviewees' opinions on gamified classes.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.21-26
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• 2022

This study touched on the effect of the different electronic course designs on the programming skills of university students. The researcher used the experimental research design of a quasi-experimental of two experimental groups to achieve the objectives of the study. The first group underwent an electronic course designed in the holistic pattern, and the second group was taught a course in a sequential pattern. This experimental design was intended to measure the impact of these two learning modes on the learners' cognitive and performance achievement of programming skills. An achievement test and observational form were the data collection tools. Data were analyzed statistically using Pearson correlation, Mann Whitney Test, and Alpha Cronbach. The findings revealed statistically- significant differences between the mean scores of the students of the first and second experimental groups in favor of the former concerning the observational form and the latter in the cognitive test. Based on the findings, some recommendations are suggested. Due to their effectiveness in the educational process, expanding using the e-courses at universities is vital. The university teachers are highly recommended to design e-courses and provide technical and material support to the e-courses user to fulfill their design purpose.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.191-201
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• 2017

The purpose of this study was to examine the learning outcome of problem-based learning as an efficient teaching method to improve the competencies of great talents for future society. A general education course of a university that was one of teaching profession courses titled "prevention of school violence and countermeasures" was provided in the form of PBL, and data were gathered, which were reflective journals, evaluation sheet and observational journals. As a result, PBL produced learning effects such as understanding and applying learning contents, fostering cooperativeness, problem-solving skills and a sense of responsibility, extended thinking and good understanding of PBL. Also, that provided a great opportunity for the students to build up their character by learning caring and by improving cooperativeness, a sense of responsibility and communicative competency, which preservice teachers should have. The findings of the study suggest that the expansion of PBL is necessary to bolster problem- solving skills, self-directed learning, cooperativeness and creativity that are competencies required in the era of the Fourth Industrial Revolution.

• Journal of The Korean Astronomical Society
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• v.55 no.4
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• pp.131-138
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• 2022

We report the discovery of four quasars with M₁₄₅₀ ≳ -25.0 mag at z ~ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000-8000 Å, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its C _IV λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ~10⁸ M_⊙ and ~0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (L_Bol < 10⁴⁶ erg s^-1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.508-526
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• 2020

This study has established a fashion illustration education plan using the contour drawing that fosters observational ability and enables creative drawing. This study developed two illustration curriculum proposals consisting of 15 weeks, combining literature and case studies. The researchers organized a step-by-step teaching plan that utilizes contour drawing according to the three stages of fashion illustration classes: foundation courses, general courses, and intensive courses. When the contour drawing is used at the beginning stage of the foundation courses of fashion illustration, it can be used as a technique to reduce the fear of students about practical skills, induce interest in illustration, and cultivate observation ability about objects. In general process, it is combined with various tools and coloring materials to strengthen expression power, and it is possible to produce detailed expressions and illustrations about human body and clothing. In intensive courses, it is expanded to the production of creative works with new aesthetics through digital techniques and mixed materials. As such, the contour drawing is expanded in various ways according to the learning contents and goals of each step, and is flexibly adjusted according to the learning content. Contour drawing has the effect of acquiring observation and expression ability, and it is analyzed as a technique that enables the production of creative illustration of students.

• The Mathematical Education
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• v.53 no.1
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• pp.1-24
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• 2014

This study examines the effects of reflective journal writing on the mathematics self-efficacy in reciprocal peer tutoring. Participants were 38 high school students in Gyeonggi province who attended at a summer intensive mathematics course for 4 weeks. This study used a mixed method. SPSS 21.0 program was used to analyze the quantitative data, and the interviews, observational journals and reflective journals of 6 students were used to analyze qualitative data. According to the results, all the subcategories of mathematics self-efficacy, - mathematics problem-efficacy, mathematics success-efficacy, mathematics learning-efficacy, and mathematics subject-efficacy - improved except mathematics occupation-efficacy. In case of mathematics success-efficacy and mathematics problem-efficacy, students revealed the greatest improvement. In conclusion, reflective journal writing in reciprocal peer tutoring could be suggested as a treatment program to improve students' mathematics self-efficacy.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.