• East Asian mathematical journal
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• v.38 no.2
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• pp.187-214
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• 2022

This study compares the functions of the Learning Management Systems (LMS) in three widely used Edu-Tech platforms, that support students' individualized learning by using the learning characteristics of the students. The rapid advances in artificial intelligence (AI) are broadening their impacts in the education industry, and play a broad role in supporting student learning. In many countries, online classes have become a norm due to the COVID-19 crisis, and the demand for Edu-Tech in classes has increased rapidly. As a result, many countries, including South Korea, are now preparing and implementing various policy measures to adopt Edu-Tech in the class setting. Therefore, in this study, we analyze and compare the structures and characteristics of the three widely used Edu-Tech platforms that support individualized mathematics learning. In particular, we compare the LMSs of the three platforms by considering the elements such as learning design, learning management, learner analysis, learning result analysis, and student management functions. The results of this study give implications in the future directions to take on how to build Edu-Tech platform models that promote students' individualized mathematics learning in public education.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.500-507
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• 2022

COVID-19 pandemic forces college education to be rapidly switched from face-to-face education into remote education. Two inconsistent findings exist in previous study about remote learning. First, studies before COVID-19 pandemic found remote learning is an effective method, which provided students with higher achievement and improved their work-life balance. However, studies showed remote learning during COVID-19 pandemic is not as effective as expected because of technical issues, lack of motivations and even mental health issues. Second, findings from studies about remote learning impacts on workload and productivity during COVID-19 are also inconsistent. Therefore, this study aims to quantitatively measure college students' workload and productivity during COVID-19 of different types of tasks to provide a comprehensive and latest evaluation on remote learning. The findings of this study show remote learning slightly increases college students' total listening and speaking tasks workload, total reading and writing tasks workload. Furthermore, phone call, in-person meeting, online meeting and email workload increase significantly in remote learning. However, productivity for both listening and speaking, reading and writing tasks decreases after remote learning but no significant changes of productivity are found.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.19-35
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• 2000

Learning by examples has proven to be an efficient method in mastering various subjects including programming languages. This study hypothesizes that the number of examples and the type of examples are two significant dimensions that influence the performance of learning programming languages by examples. A set of experiments was conducted to investigate the impacts of the two dimensions in the domain of JAVA programming. The results showed that providing two examples is more effective than providing only one example even though significantly more explanations are attached to the single example. Among the 'two-example' groups, the group that was given functionally similar examples performed better than those with functionally dissimilar examples. Explanations for these results are provided in this paper based on the behavioral patterns of individual subjects in terms of time and frequency. This paper concludes with the implications of the study results for the development of effective tutoring systems for programming languages.

• Land and Housing Review
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• v.8 no.3
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• pp.123-130
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• 2017

This study aims to empirically verify the impacts of career learning on employability and the mediating effect of job expertise in a public corporation. For the empirical analysis, I surveyed 958 employees(valid respondents) working in a public corporation. And the structural equation modeling(SEM) was used to statistically analize and test the research hypotheses. The independent variable is 'career learning', the dependent variable is 'employability' and the mediating variable is 'job expertise'. The results are as follows. The empirical analysis shows that the positive effects of 'career learning ${\rightarrow}$ job expertise', 'job expertise ${\rightarrow}$ employability' and 'career learning ${\rightarrow}$ employability' are all verified. And the mediating effect of job expertise between career learning and employability is also partially verified. So, all the proposed hypotheses are accepted. From this result, I can clearly suggest that the employees can be growing to professionals with high employability when they retire if they are voluntarily and self-motivated to set up their career plan and to enhance their job expertise. In this context, it is expected that the company should support the employees to continue to strengthen their own expertise in their job place through their mid-long term career learning plan.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.363-364
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• 2022

The COVID-19 pandemic had substantial negative impacts and caused several disruptions to the global supply chain of the shipping industry. The key challenges identified in terms of maritime manpower are the Certificates of Competency (CoC) or the expiration and/or failure to complete refresher and/or revalidation courses, which directly hinder employment retention and lost opportunities at sea. To tackle this issue directly and swiftly, the creation of the APEC SEN Distance Learning Platform was suggested and approved by APEC as part of an official project. This paper introduces the APEC-wide accessible distance learning platform with the following key topics: the organisation and operation of the platform, the themes and content to be prioritised, the process of education, training, certification, and the ways to promote accreditation, mutual recognition on CoC, education and training videos by taking collaborative actions, and the development of content.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.1001-1007
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• 2024

Recent deep learning algorithms for object detection in real-time play a crucial role in various applications such as autonomous driving, traffic monitoring, health care, and water quality monitoring. The size of small objects, in particular, significantly impacts the accuracy of detection models. However, data containing small objects can lead to underfitting issues in models. Therefore, this study developed a deep learning model capable of quickly detecting small objects to provide more accurate predictions. The RE-SOD (Residual block based Small Object Detector) developed in this research enhances the detection performance for small objects by using RGB separation preprocessing and residual blocks. The model achieved an accuracy of 1.0 in image classification and an mAP50-95 score of 0.944 in object detection. The performance of this model was validated by comparing it with real-time detection models such as YOLOv5, YOLOv7, and YOLOv8.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• Knowledge Management Research
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• v.18 no.2
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• pp.85-102
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• 2017

With rapid development of information and communication technologies, open collaboration can be eased through the Internet. Open source software, as a representative area of open collaboration, is developed and adopted to various fields. In this research, based on organizational learning theory, we examine the impacts of exploration and exploitation on innovation performance in open source software development projects. We define knowledge exploration as a number of developers from outside organization and knowledge exploitation as the ratio of member of an organization who participated in an open source software project managed by the organization. For analysis, we collect data of 4794 projects from github which is a representative open source software development platform using Web crawler developed by Python. As a result, we find that excessive exploration has curvilinear (invers U-shape) relationship on project performance. On the other hand, exploitation with enough external developers will positively impact on project performance.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.67-74
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• 2006

The purpose of this study is identifying variables that affect to learners' preference toward specific e-learning programs, using an exploratory factor analysis(EFA) method. We extract common factors that explain the correlations among variables. In the result, 8 factors were identified as main influential factors: e-learning program design(1st factor), the purpose of e-learning use(2nd factor), social and cultural issues(3rd factor), demographics(4th factor), organizational needs(5th factor), impacts of e-learning(6th factor), e-learning management(7th factor), and technical issue(8th factor).