• English Language & Literature Teaching
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• v.10 no.2
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• pp.133-158
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• 2004

The purpose of the present study was to scrutinize computer games that can motivate elementary school students through their interactive "edutainment" effects. The types of elements in computer games that students find interesting as learning media and their impact were studied. The current status of Korean computer games, issues related to learning English, and methods to stimulate the motivation and interest in learning by elementary school students were explored. A computer game model for efficiently teaching English to elementary school students through a connection between computer games and education was suggested. In this model, overall games were designed with the focus on the integration of curriculum and content subjects related to learning activities. For games not to be biased toward entertainment and to have systemized learning steps, the games are composed of an introduction, presentation, practice, production and evaluation, in that order. The model suggested by this plan and composition make it possible to approach learning efficiently with entertaining games based on a systematic learning curriculum. As shown above, developing the model of educational computer games can be seen as an opportunity, which can provide amusement and interests and a broad learning experience as an additional learning method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.553-560
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• 2022

The research of course-embedded learning evaluation method, which can be used to measure the competency of learners by evaluation of learning outcomes, has been performed for competency-based education in the university. In this paper, we propose an learning evaluation and adaptive feedback model based on learning outcomes, learning subjects, learning concepts graph, and an evaluation matrix. Firstly, we define the layered learning outcomes, a graph of learning subjects and concepts, and two association matric. Secondly, we define algorithms to calculate the level of learning achievement and the learning feedback to learners. We applied the proposed method to a specific course, "Java Programing", to validate the effectiveness of our method. The experimental results show that our proposed method can be useful to measure the learning achievement of learners and provide adaptive feedbacks to them.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.309-325
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• 2024

The purpose of this study was to develop self-evaluation criteria for objective verification and performance analysis of LINC 3.0. To achieve this goal, evaluation indicators in the fields of human resources development and skill development and commercialization were developed and their validity was verified. We investigated previous evaluation-related studies and similar cases to construct an evaluation model and system and develop indicators. The validity of the developed evaluation indicators was secured through two round Delphi surveys. As a result of the research, LINC 3.0 evaluation indicators can be divided into the field of human resources development and skill development and commercialization. A total of 66 evaluation indicators were developed. CIPP in the field of human resources development was developed with 13 categories and 38 evaluation indicators, and CIPP in the field of skill development and commercialization was developed with 12 categories and 28 evaluation indicators. The significance of this study is that it suggests a way to increase the objective verification and validity of the university industry-academia cooperation model by developing self-evaluation indicators for the LINC 3.0 project. The evaluation indicators developed in the research need to be continuously upgraded based on field usability, and it is necessary to improve the quality and competitiveness of university education by sharing and spreading excellent affairs.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1059-1065
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• 2020

In hospitals, nurses are subjectively determining the urine status to check the kidneys and circulatory system of patients whose statuses are related to patients with kidney disease, critically ill patients, and nursing homes before and after surgery. To improve this problem, this paper proposes a urine spectrum analysis system which clusters urine test results based on a hybrid machine learning model consists of unsupervised learning and supervised learning. The proposed system clusters the spectral data using unsupervised learning in the first part, and classifies them using supervised learning in the second part. The results of the proposed urine spectrum analysis system using a mixed model are evaluated with the results of pure supervised learning. This paper is expected to provide better services than existing medical services to patients by solving the shortage of nurses, shortening of examination time, and subjective evaluation in hospitals.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.25 no.3
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• pp.240-248
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• 2018

Purpose: The purpose of this study was to evaluate the validity of an educational program developed for geriatric visiting nurses in Seoul by assessing their satisfaction level and level of learning goal achievement. Methods: This descriptive research study was conducted to develop, implement, and evaluate the educational program in accordance with the ADDIE Instructional System Model. Participants were 170 nurses hired for the 2016 Seoul Metropolitan Government visiting service for older people. Based on Kirkpatrick's Training Evaluation Model, reaction and learning evaluations were conducted during and after the educational program. Data were analyzed using descriptive statistics. Results: The developed educational program consisted of basic and professional courses. The evaluations showed that participants were highly satisfied with the lectures and field placement. Over 90% of the participants achieved the learning achievement goals. Conclusion: The program developed for geriatric visiting nurses in Seoul is considered a valid educational program because of the participants' high levels of satisfaction and academic achievement.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.41-42
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• 2019

본 논문에서는 다양한 기계학습 모델을 이용한 신용 데이터 분석 기법에 대해 서술한다. 기계학습 모델은 크게 Canonical models, Committee machines, 그리고 Deep learning models로 분류된다. 이러한 다양한 기계학습 모델 중 일부 학습 모델을 기반으로 Benchmark dataset인 Credit Approval 데이터를 분석하고 성능을 평가한다. 성능 평가에는 k-fold evaluation method를 사용하며, k-fold evaluation 결과에 대한 평균 성능을 측정하기 위해 Accuracy, Precision, Recall, 그리고 F1-score가 사용되었다.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• Journal of Engineering Education Research
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• v.25 no.2
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• pp.13-21
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• 2022

In many existing studies, the analyses on the application and effect of the project-based learning model (PBL), a student-centered teaching and learning strategy, have been emphasized and carried out in various majors and courses. This case study analyzes the effects of applying a project-based learning model to the engineering accounting course for engineering students in 2021 in the context of the COVID-19 pandemic, compared with the offline course in 2019 and the simple online course in 2020. Project team consisting of 2-3 students carried out online collaborative learning activities for solving open-ended problems through the 5-step PBL procedure including presenting the final result. Except for this online PBL application in 2021, textbooks, lecture contents, assignments, and tests were implemented the same for each semester for three years. Through lecture evaluation and survey by students, the online PBL application semester showed higher effects in inducing student-centered learning, lecture satisfaction, and student competency improvement compared to the non-applying semesters, further, it was evaluated that the online PBL application to the course and evaluation method were more appropriate than other semesters. It is expected that the online PBL method and operation procedure applied in this study can be utilized as a best practice for the design and operation of various online courses for student-centered collaborative learning activities and educational effects.