• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.63-83
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• 2021

The appearance of education is also rapidly changing in social changes represented by social networks. And the development of information and communication technology is also having a widespread effect on the education field. In the era of untact caused by Covid-19, education through smart learning is having a greater effect on students as well as adult learners more quickly and broadly. In addition, smart learning is not just limited to learning content, but is developing into personalized, convergence, and intelligent. The purpose of this study is to identify the factors of ARCS motivation theory that can determine the learning motivation of smart learning users, and to empirically study the casual relationship between these factors on education achievement through practical value and hedonic value. Specifically, I would like to examine how the independent variables ARCS motivation factors (attention, relevance, confidence, and satisfaction) affect learners' education achievement through the parameters of practical value and hedonic value. To this end, a research model was presented that applied the main variables of attention, relevance, confidence, and satisfaction, which are four elements of ARCS motivation theory, a specific and systematic motivational strategy to induce and maintain learners' motivation. In order to empirically verify the research model of this study, a survey was carried out on learners with experience using smart learning. As a result of the study, first attention was found to have a positive effect on the hedonic value. Second, relevance was found to have a positive effect on the hedonic value. Third, it was found that confidence did not have a positive effect on the practical value and the hedonic value. Forth, satisfaction was found to have a positive effect on the practical value and the hedonic value. Fifth, practical value was found to have a positive effect on the education achievement. Sixth, hedonic value was found to have a positive effect on the education achievement. Through this, it can be seen that the intrinsic motivation of learners using smart learning affects the education achievement of users through intrinsic and extrinsic value. A variety of smart learning that combines advanced IT technologies such as AI and big data can contribute to improving learners' education achievement more effectively and efficiently. Furthermore, it can contribute a lot to social development.

• Journal of Korea Game Society
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• v.15 no.6
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• pp.89-96
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• 2015

The purpose of this paper is to analyze quantitative learning motivation on G learning. For the purpose the paper has analyzed the learning motivation factors through students' review on G learning which had been done at La Ballona Elementary School in Culver City, USA in 2010. On the basis of contents analysis method, it showed what factors of G learning influenced students and raised their academic motivation. Students used the positive, active words such as good, fun, learn, accomplish, easy, quest in terms of learning process, interest and achievement. They also showed future G learning intention describing terms such as love and miss. Team Quest has been especially developed for G learning class this time. Students had to help each other to solve the team quests which is different from traditional textbook. The system raised students' academic motivation.

• Journal of the military operations research society of Korea
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• v.36 no.2
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• pp.1-10
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• 2010

Learning rate is generally applied to estimate an appropriate production labor cost. Learning effect is obtained from repetitive work during the production period under 3 assumptions ; homogeneous production, same producer, quantity measure in continuous unit. However, production breaks occur frequently in Korean defense industry environment because of budget constraint and annual requirements. In this case previous learning effect can not be applied due to learning loss. This paper proposed the application of learning rate when a production break occurs in Korea defense industry. To obtain a learning loss, we surveyed various learning loss factors for different production breaks(6, 12, 18 months) from 4 defense industry companies. Then, we estimate the first unit labor hours in re-production phase after production break using Anderlohr method and Retrograde method with the result of the survey. This work is the first attempt to show a method which defines and evaluates the learning loss factors in Korean defense industry environment.

• Journal of Agricultural Extension & Community Development
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• v.23 no.3
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• pp.233-242
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• 2016

This study aimed to analyze educational satisfaction and the relationship between learning transfer and learning persistence shown after actual education targeting students who participated in the agricultural education and training. Conclusions based on the study results can be suggested as follows. First, of the factors related to learning persistence, satisfaction of educational contents turned out to be a statistically significant factor with a positive effect in the agricultural education and training. Students participating in the agricultural education and training have a conspicuous object to learn for improving ability which is necessary for and applicable to agriculture. Second, of the three factors related to learning transfer in the agricultural education and training, satisfaction of educational contents, educational facilities and satisfaction of environment turned out to have a positive effect. Third, results show that satisfaction of instructors does not affect both learning persistence and learning transfer. Lastly, in case of education and training for field practice, this study is suggesting the necessity of research by accessing in a concrete and detailed manner such as learning contents, instructors, educational facilities and satisfaction of environment from the comprehensive concept of educational satisfaction in the directivity of study related to satisfaction.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.33-42
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• 2016

This study aimed to understand the differences of the factors for learning satisfaction (learning content, system, interaction, instructor personality, and instructional context) between males and females in online graduate university settings. To examine the research objectives, a total of 88 graduate students attending online universities in Seoul, Korea participated in the survey. Among them, 66 valid responses were used for the analyses using the SPSS 21.0 statistical package. In order to figure out the differences of the factors in gender, Multivariate Analysis of Variance(MANOVA) was conducted with the five dependent factors. As a result, interaction was found to be a significant variable implying that females more actively participated in communication process. Finally, possible reasons for the results were described and suggestions were raised.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.151-152
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• 2021

The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.89-104
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• 2024

Cloud-based e-learning (CBEL) represents a promising technological frontier. Existing literature has presented a diverse array of findings regarding the determinants that influence the adoption of CBEL. The primary objective of this study is to conduct an exhaustive examination of the available literature, aiming to determine the key predictors of CBEL utilization by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A comprehensive review of 35 articles was undertaken, shedding light on the status of CBEL as an evolving field. Notably, there has been a discernible downturn in related research output during the COVID-19 pandemic, underscoring the temporal dynamics of this subject. It is noteworthy that a significant portion of this research has emanated from the Asian continent. Furthermore, the dominance of the technology acceptance model (TAM) in research frameworks is affirmed by our findings. Through a rigorous thematic analysis, our study identified five overarching themes, each encompassing a diverse range of sub-themes. These themes encompass 1) technological factors, 2) individual factors, 3) organizational factors, 4) environmental factors, and 5) security factors. This categorization provides a structured framework for understanding the multifaceted nature of CBEL adoption determinants. Our study serves as a compass, guiding future research endeavours in this domain. It underscores the imperative for further investigations utilizing diverse theoretical frameworks, contextual settings, research methodologies, and variables. This call for diversity and expansion in research efforts reflects the dynamic nature of CBEL and the evolving landscape of e-learning technologies.

• English Language & Literature Teaching
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• v.17 no.2
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• pp.93-114
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• 2011

The aim of this study is twofold. First, this study aimed to explore how Korean university students' English learning motivation is related to their English listening proficiency and study time. Second, it attempted to interpret the English learning motivation linking the two different motivation theories: self-determination theory and L2 motivational self system. The constructs of the students' L2 learning motivation were investigated with the data obtained through the questionnaire from 122 sophomore students. A factor analysis was conducted to extract the major factors of motivation. As a result, 6 factors were extracted: Intrinsic Pleasure, Identified Value Regulation, Intrinsic Accomplishment, Introjected Regulation, External Regulation, and Identified Regulation. The Interrelatedness among the assessment results on the L2 listening proficiency (pre and post test), listening study time, and motivation factors was measured by correlation coefficients. The statistical results indicated that pre-test scores were significantly related to Identified Regulation and Identified Value Regulation toward English learning, and post-test results had significant correlation with Intrinsic Accomplishment and Identified Regulation. However, no motivation subtypes showed statistical association with the students' listening study time. The results were attempted to be interpreted both under L2 motivational self system and self-determination framework to better illuminate the motivation theory with more explanatory power.

• Korean Medical Education Review
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• v.16 no.3
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• pp.156-166
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• 2014

The current study aimed to find the relationship between attention, relevance, confidence, and satisfaction (ARCS) learning motivation and class effectiveness inherent in problem-based learning (PBL) classes designed for dental and nursing students. Seventy-nine participants responded to survey items for motivation and class effectiveness after completion of their classes. The study findings were as follows. First, the differences among the $dental^{**}$, $clinical^{**}$, and $nursing^{**}$ PBL classes were identified in terms of class effectiveness (F=3.63, p<0.05) and academic achievement (F=13.9, p<0.01). Second, three learning motivation factors-satisfaction (t=4.07, p<0.01), confidence (t=2.84, p=0.01), and relevance (t=2.96, p<0.01)-appeared to determine class effectiveness in the abovementioned order. Only attention (t=2.02, p=0.05) was significantly related to academic achievement. Third, the relationship between hours of learner contribution to the PBL tasks and academic achievement was statistically significant. Therefore, we suggest the incorporation of ARCS motivation factors in PBL classes on the basis of the characteristics of each major and grade.