• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.640-650
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• 2016

This study was conducted in order to develop an education program based on simulation with problem-based learning, to apply it to nursing students, and to examine its effects on the students' learning motivation, learning strategy, and academic achievement. The subjects of this study were 69 seniors majoring in nursing. Education based on simulation with problem-based learning was applied to the students from September to October in 2015, and then a questionnaire survey was conducted on their learning motivation, learning strategy, and academic achievement. According to the results of this study, the education based on simulation with problem-based learning reduced the nursing students' other-directed motivation (external motivation), increased their self-regulation motivation (identified motivation, intrinsic motivation), and improved their use of resource management strategies. In addition, academic achievement (academic performance, and educational satisfaction) was in a positive correlation with identified motivation and learning strategies (cognitive strategy, meta cognitive strategy, and resource management strategy). In conclusion, education based on simulation with problem-based learning was found to be an effective education strategy for enhancing nursing students' autonomous motivation and improving their use of resource management strategies. Thus, it is necessary to promote the application of simulation with problem-based learning in various care situations and to study factors and parameters influencing learning related variables.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.22 no.4
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• pp.424-432
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• 2015

Purpose: In this study the time point of effects that repeat exposure to simulation-based nursing training has on stress, interest in learning, and problem-solving abilities were identified. Methods: Participants for this study were 75 nursing college students in Seoul. In a preliminary survey data were collected and measured for the general characteristics, stress, interest in learning, and problem-solving abilities of the students. Then, stress was assessed before performance of each of four-rounds of simulation training scenarios. After each simulation round, interest in learning and problem-solving abilities were assessed. Results: With respect to stress, no significant differences were found when comparing the results of the preliminary survey to those of each of the simulation-based training exercises. For the sub-items of interest in learning, interest in nursing knowledge and interest in clinical training significantly increased between the preliminary survey and the $4^{th}$ survey. Interest in lab training increased significantly at the $1^{st}$ survey. Problem solving abilities showed a significant increase from the preliminary at each of the survey points. Conclusion: Increasing the exposure of nursing students to simulation-based training enhances their interest in learning and problem-solving abilities. Therefore it is necessary to have education strategies that includes various simulation experiences for students.

• Journal of Korean Critical Care Nursing
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• v.5 no.1
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• pp.12-22
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• 2012

Purpose: This study was conducted to evaluate the learning outcomes of simulation-based practice on emergency care for patients with dyspnea in nursing students. Methods: One group pre-post experimental design with 28 nursing students was used. Simulation-based practice on the basis of SimMan Human Patient Simulator including academic lectures, simulation lab exercises and debriefing was applied for four and half hours. The learning outcomes were assessed by measuring knowledge, critical thinking, problem solving process of cognitive skills, self-confidence and learning attitudes of affective domain. Furthermore, self reported clinical performance ability of psychomotor skills was examined. Results: After the completion of simulation-based practice, there was a significant increase in the mean of following measured variables: knowledge, critical thinking, problem solving process of cognitive skills, self-confidence, learning attitudes of affective domain and clinical performance ability of psychomotor skills. Significant positive relationships were found among learning outcome measurement variables. Conclusion: Simulation-based practice is an effective method to improve cognitive skills, affective domain and psychomotor skills of nursing students. Hence, Simulation-based practice should be applied for improving current limited emergency care training for nursing students and enhancing students' competency in clinical situations.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.309-323
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• 2009

Business simulation learning is a business education method of the software applications that applied for the university's management training. Its interest is increasing as management training tools to simulate the virtual enterprise and to learn the business management knowledge, management skills, experience and problem-solving. This research was done for the feasibility analysis and management education model design of the business simulation learning. As a prior research to the introduction and operation of the university's business simulation learning curriculum, the need and literature research on the possibility recognition of classes for college students and corporate executives, a demand survey research and analysis was done and discussed the necessity and possibility of the business simulation learning for the industry and universities education area. In addition, we designed a curriculum of the business simulation learning and presented some of education models of management for the university's management training purpose by using the results of the survey research and literature analysis.

• The Korean Journal of Emergency Medical Services
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• v.15 no.2
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• pp.43-54
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• 2011

Purpose: The purpose of this study was to explore EMT-paramedic students' experience of simulation education and analyze the confidence before and after education, learning attitude and course evaluation. Method: Research survey was conducted on 38 EMT-paramedic students during November, 2011 and EMT-paramedic students' experience of simulation education was analyzed after applying head, spinal, and chest injury scenario. The confidence before and after education, learning attitude and course evaluation in gender were analyzed by Mann-Whitny U test and the difference of confidence before and after education was analyzed by Wilcoxon signed rank test and learning attitude & course evaluation were analyzed by evaluating frequency, percentage, mean, standard deviation by using SPSS WIN 17.0 program. Results: 1. Students experienced various advantages such as increasing interest and self-reflection on learning, critical thinking ability, and EMT-paramedic-role experience and recognition of importance of teamwork. Students also pointed out disadvantages such as gap between real situation and simulation, limit of time and equipments, and burden of demonstration. 2. The confidence between before and after education, learning attitude and course evaluation in gender were not significant different statistically. 3. Confidence mean score elevated from 5.53(before education) to 5.87(after education), but the difference in their confidence did not show significant difference statistically. 4. Total mean score in learning attitude after simulation education was 3.70 out of 5.00, which is considerably very high. 5. Total mean score in course evaluation was 3.89 with score of 3.83 in evaluation in learning environment and 3.99 in evaluation of debriefing. Conclusion: The finding of this study demonstrate that the simulation education can provide a safe and repetitive practice environment, improve problem-solving ability and critical thinking, and increase the confidence in prehospital emergency care; therefore, simulation may be the new effective EMT-paramedic education strategy.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.17 no.3
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• pp.371-381
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• 2010

Purpose: The purpose of this study was to develop a scenario and evaluate students' performance in simulation learning of care for patients with asthma in emergency units. Methods: Meetings of experts were used to develop a scenario based on actual patients and textbook material. An evaluation protocol was developed to evaluate the simulation learning. The scenario was used in 2006 with six groups of 26 senior nursing students who participated voluntarily. Results: The scenario was developed according to the nursing process for 15 minutes of simulation learning. The nursing students were able to demonstrate their knowledge and skills. The results showed a need to improve problem solving ability. In the self-evaluation, the students reported that simulation learning helped them to integrate their knowledge to practice and recognize their weaknesses and strengths. However, the scores for self-confidence about patient care after the simulation learning were low (4.8/10). Conclusion: The scenario in this study gave the students the experience of providing qualified and secure nursing care under conditions similar to reality. Further development of a variety of scenarios for simulation learning is needed.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.61-66
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• 2024

This paper aims to design a simulation for path-finding of objects in a simulation or game environment using reinforcement learning techniques. The main feature of this study is that the objects in the simulation are trained to avoid obstacles at random locations generated on a given track and to automatically explore path to get items. To implement the simulation, ML Agents provided by Unity Game Engine were used, and a learning policy based on PPO (Proximal Policy Optimization) was established to form a reinforcement learning environment. Through the reinforcement learning-based simulation designed in this study, we were able to confirm that the object moves on the track by avoiding obstacles and exploring path to acquire items as it learns, by analyzing the simulation results and learning result graph.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• Wind and Structures
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• v.37 no.5
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• pp.383-397
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• 2023

In the Structural Health Monitoring (SHM) system of civil engineering, data missing inevitably occurs during the data acquisition and transmission process, which brings great difficulties to data analysis and poses challenges to structural health monitoring. In this paper, Convolution Neural Network (CNN) is used to recover the nonstationary wind speed data missing randomly at sampling points. Given the technical constraints and financial implications, field monitoring data samples are often insufficient to train a deep learning model for the task at hand. Thus, simulation combined transfer learning strategy is proposed to address issues of overfitting and instability of the deep learning model caused by the paucity of training samples. According to a portion of target data samples, a substantial quantity of simulated data consistent with the characteristics of target data can be obtained by nonstationary wind-field simulation and are subsequently deployed for training an auxiliary CNN model. Afterwards, parameters of the pretrained auxiliary model are transferred to the target model as initial parameters, greatly enhancing training efficiency for the target task. Simulation synergy strategy effectively promotes the accuracy and stability of the target model to a great extent. Finally, the structural dynamic response analysis verifies the efficiency of the simulation synergy strategy.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.