• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.67-74
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• 2006

The purpose of this study is identifying variables that affect to learners' preference toward specific e-learning programs, using an exploratory factor analysis(EFA) method. We extract common factors that explain the correlations among variables. In the result, 8 factors were identified as main influential factors: e-learning program design(1st factor), the purpose of e-learning use(2nd factor), social and cultural issues(3rd factor), demographics(4th factor), organizational needs(5th factor), impacts of e-learning(6th factor), e-learning management(7th factor), and technical issue(8th factor).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.345-348
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• 2005

In this paper, we suggested an evaluation model for satisfaction of e-Learning. This model was composed decision of evaluation criteria, analysis of consciousness structure for evaluation factors using the Fuzzy Structural Modeling method, decision of weights for evaluation factors considering intersectional dependence relations and evaluation of satisfaction of e-Learning. The case study of this model was done for comparative analysis between teachers and students of e-Learning in high school.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.376-390
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• 2009

The objective of this study is to examine the factors affecting adolescents' intention to buy e-Learning web site, expecially focuses on the moderating effect of adolescents's conformity. The main results are as follows. First, teacher's ability, personalized service, easy for service search, interaction between students, service revival for joining e-Learning sites affect intention to buy e-Learning web site through perceptual usefulness, except for entertainment. Second, perceptual usefulness has positive effect on intention to buy. Third, According to conformity, personalized service has different effect on perceptual usefulness. The result shows higher in high conformity group than low conformity group. It means e-Learning companies should focus on reference group or special group to effect their marketing strategy on adolescents. Lately, preceded studies investigated using SERVQUAL or SERVERF which were suit to offline. But, this study found e-Learning factors which are suitable to online. So, the factors and the results are more useful to e-Learning companies.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.359-376
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• 2021

In this study, in accordance with the research trend that the learner's emotions expressed positively or negatively in mathematics learning or the learner's beliefs and attitudes toward mathematics learning affect the results of mathematics learning, the learner's emotions and affective factors are analyzed in the learner's own learning. A power that can be adjusted according to a goal or purpose is needed, and I tried to explain this power through meta-affect. To this end, the meaning of the definitional and conceptual factors of meta-affect was explored based on prior studies. Affective factors of meta-affect were viewed as emotions, attitudes, and beliefs, and conceptual factors of meta-affect were viewed as awareness, evaluating, controlling, utilization, and monitoring, and the meaning of each conceptual factor was also defined. In this study, the conceptual factors and meanings of meta-affect in terms of using them to help in learning mathematics by controlling them, beyond the identification or examination of the characteristics of the affective factors, which are meaningfully dealt with in the field of mathematics education.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.141-156
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• 2014

This study intended to evaluate the viability of cyber education in terms of learning satisfaction and learning achievement. The study integrated two research streams such as social presence model and learning environment model. Where the learning environment model emphasizes the components of learning aids, social presence model considers more deeply the relationships among peers and with instructors. These two research streams have been considered relatively independently. The study integrated these ideas and measured their reliabilities and validities. The results demonstrate that the two constructs are relevantly independent and both of these constructs are very important considerations for the success of cyber education. The study concludes that cyber education 2.0 requires more social presence factors than the learning environment factors such as technological development or new equipments.

• Korean System Dynamics Review
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• v.15 no.4
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• pp.129-159
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• 2014

This Study discuss exploring normative-prescriptive factors after the themes on Organizational learning categorize two descriptive/explanatory-perspectives, prescriptive/normative dimension. The former would contain information processing model, theory of action, organizing in organization, while Senge's suggestion on Learning Organization may compose the latter. Each perspective is reconstructed and reinterpreted into the causal mapping relationship founded on system thinking and SD. Underlying on the former try to discovery validities of the latter. But this study only put forward the integral-dynamic model of organizational learning without empirical simulation.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.101-108
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• 2020

One difficulty in language learning is the training of reading ability. The improvement on this ability directly affects the process and effect of language learning. At the same time, there are numerous difficulties in actual learning and teaching. Depending on current research, there is two ideas that can utilize to enhance the reading efficiency of learners. One is to amend objective factors; the other is to change subjective factors. Compared with the two ideas, idiosyncratic factors are more manipulable and controllable, so it is more valuable to conduct researches on this. But among the many subjective factors, the degree of their effectiveness is not the same, so this article attempts to compare and analyze the driving effects of two important subjective factors (peer-driven and task-driven) on reading performance. The results show that both factors can have a positive impact on reading comprehension, but different in driving effects. The task-driven has obvious short-term effectiveness; while peer-driven needs to establish its long-term effect on the basis of early coordination and cooperation among team members. Therefore, in order to maximize the achievement of learning, it is necessary to combine strengths and avoid weaknesses according to the characteristics of two factors, so as to help learners improve reading ability most efficiently.

• Journal of the Ergonomics Society of Korea
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• v.10 no.1
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• pp.29-40
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• 1991

This study deals with a quantitative analysis of the effects of colors on learning effectiveness. First, sensuous or emotional factors that school children feel about colors of teachers' clothes are measured by SD method and analyzed by factor analysis. Second, sensuous or emotional factors to enhance learning effectiveness are measured by SD method from teachers, and principal factors are extracted by factor analysis. Finally, the analysis of interaction between the effects of colors and the learning effectiveness is done using the sensuous or emotional factors found from the previous two analyses. The results are as follows: (1) For in-class concentration, the principal factors are "stable", and "near" feelings. The colors related to these feelings are black, red, and blue. (2) For question inducing, the first principal factors are "soft" and "stable" feelings, and the colors are white and black. The second principal factors are "gentle" and "refined" feelings, and the colors are orange and black. (3) For extra-curricular activity, the principal factors are "artless" and "plain" feelings, and the color is blue.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Korean Society of Child Welfare
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• no.54
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• pp.79-111
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• 2016

The purpose of this study was to examine the structural models in which early cumulative risk factors affect children's language(indicated by expressive vocabularies) and social development(indicated by peer competence) at age 3 thorough their effects on the home learning environment. To examine the hypothesized models, the data of 1,725 families from the second and the fourth waves of the Panel Study of Korean Children was used. Correlation analysis and structural equation modeling were conducted to test the models. First, the cumulative risk factors at age 1 and 3 were highly correlated, implying the stability of the risk factors over time. The more cumulative risk factors at age 1 predicted the lower level of the home learning environment at age 3, which, in turn, was significantly related to both language and social development at age 3. However, the early cumulative risk factors did not directly influence later developmental outcomes. Moreover, the cumulative risk factors at age 3 were directly related to the child's language development, but neither social development northe home learning environment. In addition, the mediational role of the home learning environment (i.e., cumulative risk factors at age 1${\rightarrow}$home learning environment${\rightarrow}$language and social development) was statistically supported. In conclusion, the early cumulative risk factors in infancy indirectly predicted children's development at age 3 through the home learning environment. The practical implications for the early intervention and support for the families with infants who are experiencing multiple risk factors were discussed.