• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.149-156
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• 2016

This paper is concerned with education courses operating practices for basic computer literacy training. In this study, we propose an example for students to effectively measure and evaluate the achievement of defined ability in the performance measurement and analysis, learning objectives and learning outcomes set in operation throughout the course. Through research and development use case presents the tools for teaching method and effective and objective measurement of the related subjects. And based on the results, we propose the possibility of utilizing NCS-based course operation and education certification. In this study, the measurement process is based on the association with the objective of the development and operation, and measurement tools, measuring tools for measuring learning outcomes associated with the curriculum design methods for the measurement and evaluation of the case of the operation of the course units of learning outcomes and the method proposed.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.200-206
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• 2020

Digital storytelling, a 21st century educational tool that combines digital media with storytelling, is considered as a powerful tool to learn foreign languages in an authentic and meaningful way. This study explored cyber university students' perceptions and experiences with a digital storytelling project implemented to enhance their English writing and speaking skills. The results of 9 week-long research indicated that most of the students' attitudes were positive: the digital storytelling project was effective to increase their English skills, learning motivation and interest in learning English. Most students perceived that the process writing approach and videotaping tasks provided them with good opportunities to develop their English writing and speaking skills. Although many students had difficulties selecting a topic and writing a script in the initial stage, they could handle with these challenges with the help of professors' feedback and successfully completed their projects. After completing the project, most students expressed a strong sense of accomplishment. These results suggest that the digital storytelling project effectively met cyber university students' learning needs and facilitate their English learning.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.307-316
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• 2020

Machine learning model data is highly affected by performance. preprocessing is needed to enable analysis of various types of data, such as letters, numbers, and special characters. This paper proposes a development environment that aims to process categorical and continuous data according to the type of missing values in stage 1, implementing the function of selecting the best performing algorithm in stage 2 and automating the process of checking model performance in stage 3. Using this model, machine learning models can be created without prior knowledge of data preprocessing.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.203-209
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• 2021

When carrying out computer programming, the process of checking and correcting errors in the source code is essential work for the completion of the program. Non-computer majors who are learning programming for the first time receive feedback from instructors to correct errors that occur when writing the source code. However, in a learning environment where the time for the learner to practice alone is long, such as an online learning environment, the learner starts to feel many difficulties in solving program errors by himself/herself. Therefore, training on how to check and correct errors after writing the program source code is necessary. In this paper, various types of errors that can occur in a Python program were described, the errors were classified into simple errors and complex errors according to the characteristics of the errors, and the distributions of errors by Python grammar category were analyzed. In addition, a coding learning process to refer error lists was designed to present a coding learning method that enables learners to solve program errors by themselves.

• Information Systems Review
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• v.20 no.4
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• pp.25-41
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• 2018

Applying predictive analytics to enterprise processes is an effective way to reduce operation costs and enhance productivity. Accordingly, the ability to predict business processes and performance indicators are regarded as a core capability. Recently, several works have predicted processes using deep learning in the form of recurrent neural networks (RNN). In particular, the approach of predicting the next step of activity using static or dynamic RNN has excellent results. However, few studies have given attention to applying deep learning in the form of dynamic RNN to predictions of process performance indicators. To fill this knowledge gap, the study developed an approach to using process mining and dynamic RNN. By utilizing actual data from a large domestic company, it has applied the suggested approach in estimating timely stocking in purchasing process, which is an important indicator of the process. The analytic methods and results of this study were presented and some implications and limitations are also discussed.

• The Journal of Korean Academic Society of Nursing Education
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• v.19 no.3
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• pp.371-383
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• 2013

Purpose: The purpose of this study was to develop a module for infants in pediatric nursing based on problem-based learning (PBL) and to analyze its effect. Methods: The data used in the study was collected from 79 junior nursing students. For the analysis of the module effect, one group post-test design was applied for measurement of academic achievement. Results: The module for infants is comprised of a 4-hour PBL module. The academic achievement tests are composed of formative (tutor evaluation, self-evaluation) and summative evaluations. The self-evaluation revealed the strong need for PBL and the importance of PBL in the problem solving process and integrative thinking. The mean score of the subjects' learning satisfaction, problem solving process and concept map assessments were all above 50 percent. There was no significant difference in the mean score between the infant PBL and other teaching methods. Conclusion: The PBL proved to be a useful learning method to promote the critical and integrative thinking process.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.244-249
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• 2000

Using a sample of 83 secondary school science teachers, this study investigated the views on science and learning which they have. Questionnaires used in this study were the same that Kwon & Pak(1995) administered. Both 'Questionnaire for Perception of Nature of Science' and 'Questionnaire for Constructivistic Views of Learning' were 11-point scales. Data analyses were done by using t-test and ANOVA. The secondary science teachers showed a relativistic, deductivistic, instrumental, and process-oriented views on science. The younger age of the teachers, the more emphasis on process rather than content. Female teachers had more relativistic, instrumental, and process-oriented views than male teachers had. The teachers sampled in this study showed a constructivistic view on learning. Finally, the teachers' views of science and learning were closely related each other.