• Korean Medical Education Review
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• v.23 no.1
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• pp.11-22
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• 2021

This review of the literature explored the experiences and effects of peer-assisted learning in basic medical education. Peer-assisted learning is most commonly utilized to teach clinical skills (including technical skills) and medical knowledge (76.4%). It has also been used, albeit less frequently, to facilitate small-group discussions including problem-based learning, to promote students' personal and professional development, to provide mentoring for career development and adaptation to school, to give tutoring to at-risk students, and to implement work-based learning in clinical settings. Near-peer learning is a common type. The use of active learning techniques and digital technology has been increasingly reported. Students' leadership had frequently been described. Student tutor training, programs for teaching skills, institutional support, and assessments have been conducted for effective peer-assisted learning. There is considerable positive evidence that peer-assisted learning is effective in teaching simple clinical skills and medical knowledge for tutees. However, its effects on complex skills and knowledge, small-group discussions, personal and professional development, peer mentoring, and work-based learning have rarely been studied. Additionally, little evidence exists regarding whether peer-assisted learning is effective for student tutors. Further research is needed to develop peer-assisted learning programs and to investigate their learning effects on student tutors, small-group discussion facilitation, personal and professional development, peer mentoring, and peer-led work-based learning in the clinical setting in South Korea. Formal programs and system advancement for a student-led learning culture is needed for effective peer-assisted learning.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.93-102
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• 2018

This article aims to present an effective and systematic learning methodology of game curriculum which is oriented convergence education. In particular, I will present the 4PBL model reflecting the trend of the changing times from teacher-centered learning to learner-centered learning environment. The 4PBL model consists of Personal based Learning, Problem based Learning, Project based learning and Performance based Learning. In this article, I will explain the concepts and characteristics of PBLs at each stage by providing concrete examples of game education courses. Such an attempt may have a meaningful value in that it can suggest a learning environment in which knowledge can be structured subjectively in a changing educational paradigm.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.127-139
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• 2008

The value of up-to-date Mobile PC such as UMPC (Ultra Mobile Personal Computer) is recognized greatly in learning environment that busywork such as characteristic of transfer easy and real time communication possibility etc. and conversation with a colleague student, free sending of studying data and public ownership etc. is required. Wish to recognize whether is acting relevant role in u - unfold learning that inflect UMPC in integration research model, and UMPC is u searching for relevant element at studying activity unfolding process u - integration Inquiry-Based Learning that present in Korean education & research information service (KERIS) at fifth-year student science time In primary school in this research. This research result could take charge role of UMPCs' studying-activity though there is persistent feedback with teacher among studying-activity although UMPC's role is utilized on constituent that can be related with studying-activity in learning process.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.69-75
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• 2011

This paper presents a model of e-learning based on ubiquitous computing configuration. First of all, we survey the advanced e-learning systems for foreign and domestic universities. Next we propose the optimal e-learning model based on ubiquitous computing configuration. The proposed e-learning model as following. we propose the e-learning system's hardware and software configurations, that are server and networking systems. Also, we construct the proposed e-learning systems's services. There are attendance and absence service, class management service, common knowledge service, score processing service, facilities management service, personal management service, personal authorization issue management service, campus guide service, lecture-hall management service. Then we propose the laboratory equipment management service, experimental materials management service etc. The proposed model of e-learning based on ubiquitous computing configuration will be able to contribute to the next generation university educational paradigm.