• Journal of the Korean earth science society
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• v.31 no.4
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• pp.392-402
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• 2010

The purpose of this study is to analyze and evaluate the self-directed learning contents of Earth science area in the basic course of the 7th grade. For this purpose, we applied the 'Cyber Home Study Content Quality Control Tool' presented in 'Elementary Secondary Education e-Learning Quality Management Guidelines (Ver.2.0)' of Korea Education & Research Information Service (2008). The results of contents analysis are as follow: First, it was presented that the study guide introduced the contents which should be studied for one class, properly. And it was not analyzed that the diagnosis assesment was not completed in the initiative study; Second, it was possible to study choosing the contents fitting the learner's level of learning in the main study, it was comprised of about 15 minutes. Third, it was performed without feedback for incorrect answers in the learning assessment, just the number of wrong questions. And the learning arrangement present the important contents learned in that class, summarizing and arranging again. The results of content evaluation are as follows: First, a big difference was not showed against the needs analysis, instructional design, interaction in each class. And the evaluation of the ethics was not included a word or sentence not suitable. The evaluation of copyright, it was analyzed that Work within the content display in compliance with international copyright Second, the evaluation of instructional design presented mainly the description of a simple picture based, the visible resources like flash card were poor. And in the evaluation of Supporting System, it was presented that the contents were installed so that it was freely available for learners. But it was analyzed that there was no memo-function learners were able to jot down something during the studying contents. And in the evaluation for evaluation, the clear valuation basis about the described content was not presented. So there were slightly differences for each class. Third, in the evaluation and analysis for learning content, it was presented that there were some big differences for each class because it was not composed of the latest information, not corrected and complementary.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.876-883
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• 2009

Learning Diagnosis & Prescription Service(LDPS) in Cyber Home Learning System is a educational service which provides customized learning contents based on student's academic level and individualized counseling and comments after diagnosing learner's study habits beyond the past e-Learning systems which offer the same contents to different students. For a national point of view, it is a crucial project in public education to achieve the goals of the next-generation e-Learning service by making a lot efforts both in time and money. However, those efforts has been made, not in terms of providing a better quality of service and a better user experience in a effective and enjoyable way, but in terms of developing the technology-driven system. Therefore, in this study, two types of usability evaluations has been conducted in order to enhance a user experience on the LDPS. One is the expert reviews by utilizing the usability evaluation tools (heuristics) which was focused on educational contexts developed by Suh Young-suhk(2007). The other is the user testing with students who have done think-aloud during the evaluation, remembering their retrospective experience with LDPS, and the interview with teachers & service operators were conducted. As the implications on the research, this is an effort to provide an user-friendly educational system for the students nationwide.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.393-399
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• 2012

New and creative skills must be developed, and adapted into a lesson, to motivate learners to acquire a second language easily and enjoyment, Multimedia tools which are of interest to learners, such as; smart phones, computers, and notebooks with wireless internet compatability, will provide learners opportunities to study, and do their work practically anywhere and anytime. Recently, podcasts, which are a type of digital media, consisting of a series of audio episodes or video files, subscribed to and downloaded through web syndication, or streamed online to a computer or mobile device, are used to facilitate ESL (English as a Second Language) learning. Development of a variety of teaching methods, using multimedia tools, is needed. There are advantages and disadvantages to using a variety of multimedia tools. The current research aims to study its characteristics and application, in order to maximize their effective use, in English education. The current study suggests a ubiquitous learning environment using multimedia content tools, internet media, video teleconferencing, cyber-learning, and one-to-one videos used in conjunction with, or as a digital textbook for the English lesson. This study also investigates future educational changes, using state-of-the-art equipment for the self-learning experience, and will present a new direction in English education, through a variety of instructional devices and a marginalized class system model.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.163-180
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• 2019

This study introduces a development of calculus contents which makes to understand the main concepts of calculus in a short period of time and to enhance problem solving and computational thinking for complex problems encountered in the real world for college freshmen with diverse backgrounds. As a concrete measure, we developed 'Teaching and Learning' contents and Python-based code for Calculus I and II which was used in actual classroom. In other words, the entire process of teaching and learning, action plan, and evaluation method for calculus class with Python based coding are reported and shared. In anytime and anywhere, our students were able to freely practice and effectively exercise calculus problems. By using the given code, students could gain meaningful understanding of calculus contents and were able to expand their computational thinking skills. In addition, we share a way that it motivated student activities, and evaluated students fairly based on data which they generated, but still instructor's work load is less than before. Therefore, it can be a teaching and learning model for college mathematics which shows a possibility to cover calculus concepts and computational thinking at once in a innovative way for the 21st century.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.