• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.781-786
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• 2009

The cloud computing environment is a new trend of web based application parts. It can be IT business model that is able to easily support learning service and allocate resources through the internet to users. U-learning also is a maximal model with efficiency of the internet based learning. Thus, in this research, we proposed a design of u-learning's teaching and learning model that is applying the internet based learning. Proposal method is to fit u-learning and has 7 steps: Preparing, planning, gathering, learning process, analysis and evaluation, and feedback. We make a cloud u-learning server and cloud LMS to process and manage the service. And We also make a mobile devices meta data to aware the model.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.55-63
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• 2005

Recently, web-based E-learning systems have entered the spotlight by providing new learning environments that break down spatial and temporal limitations. The key to building the web-based E-learning system is in determining how to effectively use the system and to evaluate the degree of learning achieved by the students that use it. In traditional off-line learning systems, we can evaluate students by counting how many questions, designed to evaluate their learning achievement, he or she answers correctly within a predetermined time limit. But this method would make individualized learning, a strong point of E-learning systems, impossible because these systems provide same learning strategy to all students even though they achieve a different level of learning. Therefore, in this paper, I will find any relationships between given test answers using fuzzy implication theory, I call these fuzzy correlations, and then generate evaluation results that are reflected in those relationships. I will compare the differences between this evaluation method and a traditional evaluation method where a student takes a test to evaluate his or her learning achievement after some learning period. Finally, I will discuss how we can use these results in individualized learning.

• Journal of Engineering Education Research
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• v.18 no.2
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• pp.77-88
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• 2015

Recently, there has been an increasing interest in 'Flipped Learning,' an IT-based learner-centered teaching-learning method corresponding to meet the paradigm of the future education. For smooth Flipped Learning, there are three steps in total: a pre-class should precede; then, in the structure of classes in the classroom, in-class learning among peer learners should be done; and lastly, the operation of a post-class should be done. For successful Flipped Learning, class elements in each step should be designed with a time difference, interconnected so as to achieve a single educational objective. However, it was found that there was a limitation in that the teaching-learning model of the preceding Flipped Learning consisted of the order of analysis, design, development, implementation and evaluation as general procedures, so it would not sufficiently consider the situations of Flipped Learning only. On this background, this thesis proposes a differentiated Flipped Learning model for mastery learning in a subject of an institute of technology as a model of systematic instructional design and presents a case of a class applied to an actual subject of computer engineering.

• Journal of Engineering Education Research
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• v.23 no.6
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• pp.40-50
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• 2020

This paper proposes learning modules as a kind of integrated instruction model for soft skills program outcomes to enable CEA. Learning modules consist of course learning objectives(outcomes) described in detail, learning content(elements), learning activities(teaching learning methods), evaluation methods, evaluation rubrics so that they can be evaluated based on the performance criteria of the program learning outcomes. The unit of time for the learning module is 50 minutes. If this learning module is applied, it is expected that the soft skill program outcomes can be evaluated in the technical course. As a result of the expert feasibility study, the positive answers were much higher than the negative answers in most of the questions about the composition of the learning module or the method of managing the class.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.505-510
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• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.107-118
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• 2004

This study aims to design and implement individual complete learning system based on IPI(Individually Prescribed Instruction) model. Most of current web based learning systems do not consider individual students' ability and just follow the sequence of instructing contents $\rightarrow$ providing problems $\rightarrow$ presenting the result of evaluating. However, this system focuses on individual ability prior to studying subjects. In individual complete learning system, it is acknowledged that a period and a pace to complete each task will differ from students to students, therefore until they complete the whole unit, they are not allowed to move onto the next unit. After completing each unit, there will be a process of evaluating students' performance. It is necessary to show the correct completion of 80% of the evaluation to move onto next step; for those who are evaluated as inadequate to move on, an individual supplementary instruction will be provided. Therefore, this study intends to supplement the deficit of prior learning and provide feedback dependent on individual's learning ability so that the goal of Individual Whole Complete Learning could be accomplished.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.779-786
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• 2018

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.161-168
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• 2007

In this paper, we proposed the mathematics learning evaluation system between teachers and students using the web. The proposed web-based evaluation system lets learners make up their lesson in a self-oriented and effective way, by letting instructors diagnose learners level of understanding learned contents and letting learners take part in evaluation as well. The system also lets instructors easily make out items for evaluation by using hangul(word processor) and present them on the web. With the help of this web-based mathematics learning site and mathematics learning evaluation system, learners can perform self-oriented loaming and approach various kinds of problems. In addition, students can check with answers and have feedback on the spot. As a result, students can solve lack of understanding on the learned contents.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.62-68
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• 2022

Evaluation as learning is important for the learner competency test, and the applicable method is studied. Assessment is the role of diagnosing the current learner's status and facilitating learning through appropriate feedback. The system is insufficient to enable process-oriented evaluation in small educational institute. Focusing on becoming familiar with the AI through experience can end up simply learning how to use the tools or just playing with them rather than achieving ultimate goals of AI education. In a previous study, the experience way of AI education with PLAY model was proposed, but the assessment stage is insufficient. In this paper, we propose ELAS (Experiential K-12 AI education Learning Assessment System) for small educational institute. In order to apply the Assessment factor in in this system, the AI-factor is selected by researching the goals of the current SW education and AI education. The proposed system consists of 4 modules as Assessment-factor agent, Self-assessment agent, Question-bank agent and Assessment -analysis agent. Self-assessment learning is a powerful mechanism for improving learning for students. ELAS is extended with the experiential way of AI education model of previous study, and the teacher designs the assessment through the ELAS system. ELAS enables teachers of small institutes to automate analysis and manage data accumulation following their learning purpose. With this, it is possible to adjust the learning difficulty in curriculum design to make better for your purpose.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.