• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.90-99
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• 2023

This paper deals with studying strategy about impact of deep learning (DL) on the factor of Korean economic growth. To study classification of impact factors of Korean economic growth, we suggest dynamic equation of microeconomy and study methods on economic growth impact of deep learning. Next step is to suggest DL model to dynamic equation with Korean economy data with growth related factors to classify what factor is import and dominant factors to build policy and education. DL gives an influence in many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently, young generations will take a big impact on their job selection because generative AI can do well as much as humans can do it everywhere. Therefore, policy and education methods should be rearranged as new paradigm. However, government and officers do not understand well how it is serious in policy and education. This paper provides method of policy and education for AI education including generative AI through analysing many papers and reports, and experience.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제27권1호
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• pp.129-150
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• 2024

This study utilized South Korean elementary and middle school student data to examine the longitudinal change trajectories of learning motivation types according to the longitudinal change trajectories of mathematics academic achievement. Growth mixture modeling, latent growth model, and multiple indicator latent growth model were used to examine various change trajectories for longitudinal data. As a result of the analysis, it was classified into 4 subgroups with similar longitudinal change trajectories of mathematics academic achievement, and the characteristics of the mathematics subject, which emphasize systematicity, appeared. Furthermore, higher mathematics academic achievement was associated with higher self-determination and higher academic motivation. And as the grade level increases, amotivation increases and self-determination decreases. This study suggests that teaching and learning support using this is necessary because the level of learning motivation according to self-determination is different depending on the level of mathematics academic achievement reflecting the characteristics of the student.

• 한국융합학회논문지
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• 제9권1호
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• pp.95-102
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• 2018

최근 지식 기반사회의 도래에 따라 직장인들에게도 끊임없는 자기 학습이 필요하다. 특히 희망이 강한 사람은 자기주도학습이 강한 것으로 나타났는데 그 사이에서 심리적인 특성이나 신념인 성장 마인드셋과 Grit이 매개역할을 할 것으로 판단된다. 이에 본 연구는 직장인들의 희망과 자기주도학습 사이에서 성장 마인드셋과 그릿의 매개효과를 파악하는데 연구의 목적을 두었다. 연구대상은 서울, 대전, 충남, 충북 지역에 위치하는 총 32개의 직장에서 선정하였고 총 368명이었다. 자료 분석은 빈도분석, 상관분석 및 구조방정식 모형 분석을 실시하여 수행하였고 주된 연구결과는 다음과 같다. 첫째, 상관분석 결과 희망과 성장 마인드셋, 그릿, 자기주도학습의 모든 하위 요인에서 유의한 정적인 상관관계가 있었다. 둘째, 경로분석 결과 희망은 자기주도학습에 직접적인 영향을 미치고 있었다. 셋째, 희망은 성장 마인드셋과 그릿을 매개로 하여 간접적인 영향을 미치고 있었다. 끝으로 본 연구의 제한과 직장인들의 자기주도학습을 높이기 위하여 희망, 성장 마인드셋, Grit을 촉진시키기 위한 정책적 함의를 하였다.

• 디지털융복합연구
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• 제5권1호
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• pp.131-140
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• 2007

This paper studies characteristics of e-Learning users' motivation, attitudes and satisfaction. For this purpose, surveys were conducted of university students who choose e-Learning as one of main learning tools. The research found that stable service and proper service charges were critical to the loyalty to e-Learning service. It also showed that both quality and quantity of contents were very important factors to e-Learning growth.

• 한국수학교육학회지시리즈A:수학교육
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• 제50권1호
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• pp.103-118
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• 2011

The study examined whether the relation between mathematics self-efficacy and mathematics achievement was partially mediated by the learning strategies, using latent growth model analyses. It was also examined the auto-regressive, cross-lagged (ARCL) panel model for testing the stability and change in the relation of mathematics self-efficacy and learning strategy over time. The study analyzed the first-year to the third-year data of the Korean Educational Longitudinal Survey (KELS). The result of ARCL panel model analysis showed that earlier mathematics self-efficacy could predict later learning strategy use. There were linear trends in mathematics self-efficacy, learning strategy, and mathematics achievement. Specifically, mathematics achievement was increased over the three time points, whereas mathematics self-efficacy and learning strategies were significantly decreased. In the analyses of latent growth models, the mediating effects of learning strategies were overall supported. That is, both of initial status and change rate of rehearsal strategy partially mediated the relation of mathematics self-efficacy and mathematics achievement. However, in elaboration and meta-cognitive strategies, only the initial status of each variable showed the indirect relationship.

• 산업융합연구
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• 제21권9호
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• pp.1-9
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• 2023

본 연구는 성장마인드셋과 진로적응성 간 관계에서 학습몰입의 매개효과를 검증하는데 목적이 있으며 충북 소재 4년제 대학에 재학 중인 학생들로부터 수집한 203개의 응답 자료를 바탕으로 진행되었다. SPSS 26.0을 이용해 빈도분석과 기술통계분석을 하였고 확인적요인분석을 기반으로 측정모형을 평가한 후 수행한 경로분석은 AMOS 26.0 프로그램을 활용하였다. 연구 결과, 성장마인드셋은 학습몰입에 직접 정(+)의 영향을 미쳤으며(𝛽=.403, p<.001), 학습몰입 역시 진로적응성에 정(+)의 영향을 미쳤다(𝛽=.393, p<.001). 또한 성장마인드셋과 진로적응성의 관계에서 학습몰입은 완전매개효과를 보였다. 이는 성장마인드셋이 진로적응성에 영향을 미치기 위해서는 학습몰입이 필수적으로 함께 고려되어야 한다는 것을 의미한다. 본 연구는 급변하는 진로 환경에 효과적으로 대응하기 위해 진로적응성을 향상시키는 것의 중요성을 강조했으며 이를 위해서는 성장마인드셋 함양과 함께 학습몰입 환경 구축이 중요함을 시사하고 있다.

• 산업경영시스템학회지
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• 제20권43호
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• pp.175-195
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• 1997

The learning curve has an important effect the growth of corporation. But, in Korea, the study and inference on the learning rate of each industry are unprepared, and so, Korean industires have difficult in productivity and cost. At this point, this study infers the learning rate of the oil industries and investigates the productivity and growth of them. In conclusion, this study presents the direction of the oil industries' development. With the intention of this objects, this study seizes the status which is concerned the total quantity, the operating rate, the plant capacity, the indicators concerning productivity, the investment of R & D and the scales, and then, infers and verifies the relevancy in connection with the learning rate. In the oil industry, the average rate of learning is 65.96% from 1982 to 1994 which the total quantity and the average operation time are used to infer the rate. To observe the low rate within a same period of time, this study takes the consequences that the learning rate is almost indentical with them each year. This steady state is caused by a difference between the employee and the decision maker about the acquirement and assimiliated of technology. When the high-quality technologies posses the environment to applicate in the scene of labor with them, this technology applies to the productivities. As the learning rate increases, the productivity has more effectiveness. The result of analysis about the effectiveness of the learning rate follows that the R & D unfoldes to exist and does not contribute to the growth of the oil industry. To analyze the variables of the growth, such as the learning rate, the investement of R & D, the operating rate and the gross value added to property, plant and equipment, the model is established and examined. The business strategy in the oil industry must be developed to achive the internal growth as well as the external.

• 대한기계학회논문집A
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• 제20권4호
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• pp.2752-2759
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• 1996

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).