• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.295-303
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• 2022

This study compares two cases in which flipped learning is applied in the active learning classroom (ALC) and fixed classroom of advanced engineering education. To this end, the difference in pre-learning, academic achievement, and class satisfaction between ALC and fixed classroom flipped learning were compared. The results revealed that students in ALC flipped learning watched more video lectures for pre-learning than those in the fixed classroom flipped learning and achieved higher scores on final tests, though they obtained lower points on midterm exam. In addition, examination of class satisfaction with questions about class factors, instructor factors, and overall satisfaction revealed that ALC flipped learning showed higher satisfaction in all factors than the fixed classroom flipped learning. This case study suggests that the ALC environment, a learning space built to facilitate learner-centered activities, is more effective for flipped learning that requires active interaction in the classroom.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.35-45
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• 2017

This study aims to explore the effect of quality factors and learning styles on users' satisfaction in e-Learning context. For this purpose, statistical methods such as reliability test, factor analysis, ANOVA, regression analysis were carried out using the survey data from university students. The quality factors of e-Learning were classified into contents, system, service, and interpersonal activities while learning styles were classified into positive-cooperative, self-directed, environmental-dependent, and passive styles. The results showed that each quality factors of e-Learning has a strong positive effect on user satisfaction, and self-directed group has higher satisfaction than other groups. Learning styles have moderating effects on the quality-satisfaction relationship, and especially, the group of passive learning style has a strong moderating effect on the interpersonal activities. Theoretical and practical implications and future research directions are drawn from these findings.

• 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 Association For Science Education
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• v.31 no.7
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• pp.1025-1039
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• 2011

Even though many researchers have reported that everyday contexts can arouse students' interests and improve their science learning, the connection between everyday context and physics learning is not yet clearly discussed. In our study, at first, we assumed five guidelines for helping the development of teaching materials for physics learning in everyday context. Based on these guidelines, we developed teaching materials for understanding basic optics and applied these materials to ninth grade students. From the positive responses of students and science teachers about the developed materials, we could confirm that the guidelines were reflected well in the materials. And also, it was found that students and teachers wanted to learn or teach context-based physics in future classroom learning. However, all students do not receive benefits from learning physics in everyday context. By analyzing students' actual learning processes and interviews with them, we found five potential impeding factors which could hinder students' successful learning of physics in everyday context. As a result, we suggested five recommendations for overcoming these impeding factors.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.83-100
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• 2020

In this study, the researcher intends to examine the influence of the class factors of universities for computer accounting education upon learning ability and learning performance, with the current students or the graduates from the universities in Busan and Gyeongnam regions. The findings of this study could be summarized as follows: First, the hypothesis on the influence of the self-efficacy of the students upon class commitment and motivation to participate in learning was supported. Second, commitment and motivation had a significant impact on class performance, while the satisfaction with the class had not an impact on motivation.

• 대한공업교육학회지
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• v.30 no.1
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• pp.19-36
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• 2005

The purpose of this study is to develop the Public Practice Center's teaching-learning model by applying blended learning strategies which is complementary to the expected problems such as expansion of the educational object and diversity of the curriculum to maximize the educational effect and to analyze activation types of the Practical Practice Center to expand the Public Practice Center's function and role by studying the document. Blended Learning Strategies are established in consideration of the following eight (8) factors ; learning environment, learning purpose, learning contents, learning time, learning place, learning type, learning media, type of interaction. It is redesigned and amended to the KEDI's individual confirmation instruction model for skill learning (1975) which is considered to be effective in the filed of education by applying features, educational contents of the Public Practice Center's teaching and merit of Blended Learning Strategies simultaneous. This model is composed of six (6) steps as shown below; 1. Understanding on the purpose and orientation 2. Observation for demonstration of fundamental skill 3. Ex on-line learning 4. Acquirement of element skill 5. Confirmation for acquirement of fundamental skill 6. After on-line learning. Further to this, this model is designed so that the above eight factors will be applied to the students effectively and the merit of e-learning and off-line practice will be mixed to the learner's expectation and satisfaction.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Korea Journal of Hospital Management
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• v.9 no.2
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• pp.1-22
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• 2004

This study was designed to suggest a learning organization in a medical center by examining the factors to influence effectiveness of the learning organization. We collected the data of 586 persons who participated once or more times in the learning organization managed from 2000 to 2002 by Y Medical Center located in Seoul, and included the data of 285 persons in the final analysis. The results of the study are summarized as follows. First, as the results of examining the regression coefficients to predict the effectiveness of and satisfaction with the learning organization through the learning level, learning method and learning organization constructing level as the general variables, the important influential factors were shown as follows: 1)knowledge creation, knowledge storing, private learning, organizational learning, and learning organization construction of occupational and human levels as the factors to predict the working competency; 2) learning organization construction of the human level as the factors to assume the duty satisfaction; 3) gender, working years, private learning, team learning and organizational construction level for the prediction of the organizational commitment; and 4) medical technical service, knowledge creation, organization learning, and constructing level of the environmental and human levels for the assumption of the satisfaction with experience in the learning organization. Based on the study results of the effects in managing the learning organization, we can conclude the followings. First, the members who are in various working positions and occupations need to continuously participate in the learning organization. Second, to raise the organizational outcome from the management of the learning organization, it is necessary to establish systematic concepts in the constituents of the organizational effectiveness such as working competency improvement, duty satisfaction and organizational commitment, and the experience satisfaction of the learning organization. Finally, the future of the organization depends on the learning competencies of the organization members. To continuously exist and develop the organization, the private learning of the organizational members should be constantly spread and shared over the organizational level, and the usual innovations such as repetitive and habitual organizational learning should be generally tried out throughout the whole field of the management.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.65-73
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• 2021

This study investigated differences in learning styles and learning strategies according to learning factors: major fields, achievements, and grades and differences in learning strategies according to learning styles for college students. Unlike previous studies that analyzed differences focused on the dominant learning style, the learning style was subdivided into a balanced and dominant learning style. In the analysis of the 179 participants in M colleges, it was found that the difference between the learning style and the learning strategy according to the learning factors was not significant. But, there was a significant difference in the use of cognitive strategies according to the learning style in the dimension of information input, and in the use of all strategies according to the information processing style. It was analyzed that active learners had a high level of using cognitive strategies, visual learners had a high level of using external strategies, and balanced learners had a high level of using internal strategies. Based on the results, the training strategies to understand the learning style and to improve the level of use of the learning strategy in the learning competency improvement program was proposed.

• The Journal of Korean Association of Computer Education
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• v.19 no.6
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• pp.15-23
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• 2016

Recently the era of software integration which is expressed in words of the fourth industrial revolution has begun. Thus the need of the software education for the non-major preservice teachers who are cultivating future talent has been increasing and it is necessary to foster a positive attitude toward software education of non-major preservice teachers. The purpose of this study is to verify the causality between the computer learning attitude factors of non-major preservice teachers in the software education class. To analyze the causality, we performed correlational analysis and regression analysis between the exterior factors of attention, self-learning, application of learning and the other interior factors of computer learning attitude. As a result, the significant factor of attention was interests, and the significant factor of self-learning was superiority, and the significant factors of the application of learning were the sense of purpose and the motive of accomplishment.