• Journal of Korean Home Economics Education Association
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• v.30 no.1
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• pp.29-42
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• 2018

The purposes of this study were to develop and apply teaching-learning plans using ARCS strategies to improve character of middle school students and analyze whether a home economics class helps to give positive effects on youth character change. Nine-period classes were conducted for 110 male students in 4 classes of M middle school in Gwangju metropolitan city for 5 weeks from March 6 through April 7 in 2017. The effectiveness of classes were examined with learners' class assessment and pre- and post- character index test. The research steps and results in this study are as follows. First, the teaching-learning plans for 9 periods were developed for the character education class of the chapter 'Understanding families'. These teaching and learning course plans were designed to enhance learner's interests in learning using ARCS motivational strategy and improve character of middle school students in consideration of character elements. In the chapter 'Understanding families' of Technology Home Economics in middle school, the teaching-learning plans for 9 periods, 14 student activity sheets, and 2 powerpoint materials for teaching and learning were developed. Second, students who had the character education classes using ARCS motivational strategy showed significant differences in all character elements. Therefore, the character education class using ARCS shows positive effects to build up character of middle school students. Third, the character education classes using ARCS motivational strategy increased the class satisfaction of learners. The character education class teaching and learning course plans and learning materials in Technology Home Economics using ARCS motivational strategy will be used as a basic resource to build up students' character in the future.

• Journal of Korean Home Economics Education Association
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• v.25 no.2
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• pp.1-19
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• 2013

The purpose of this study was to develope and implement a project teaching learning process plan in order to improve a creativity and character for 'residential space utilization' section of Technology Home Economics in middle school. The teaching learning process plan consisting of 15-session lessons had been developed and implemented according to the ADDIE model mixed with 6 project learning steps. In the development stage, 8 activity materials(7 individual and 1 group activity sheets) and 7 teaching learning materials(2 sets of pictures & photos, 4 moving pictures and 1 space plan resources book) were developed for the 15-session lessons. The plans applied to 5 classes 163 students in the second grade of G middle school in Gwangju during Oct. 17th to 18th of Nov. 2011. The results from the survey and portfolio showed that the 15-session lessons had overall achieved the general goal of the project teaching learning process plan to improve a creativity and character. Students were stimulated by individual and group activities with creativity and character elements in the class. The students evaluated the whole process of 15 lessons were interesting and helpful to improve creativity and consideration and cooperation of aspect of character. The individual and group results of the portfolio were excellently and creatively done with the average of nearly 85% points. The researcher also found the improving process of students in the whole classes. This plan might apply to other parts of housing as well as various other areas of home economics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.