• 한국게임학회 논문지
• /
• 제11권5호
• /
• pp.79-88
• /
• 2011

본 연구는 스마트폰으로 인식이 가능한 QR코드를 이용한 퀘스트 기반학습을 적용하여 교육 현장에서 학습방법으로서 가능성을 확인하는데 있다. 퀘스트 기반학습의 적용은 초등학교 3학년 32명을 대상으로 1개월간 총 8차시에 걸쳐 적용되었다. 학습활동 후 학습동기의 네 가지 하위요인에 대한 다변량분석을 하였다. 연구결과, 퀘스트 기반학습은 동기하위요인 중 주의집중과 자신감 요인의 동기를 촉진하는 긍정적인 결과를 보였다. 게임형태의 퀘스트 기반학습은 다양한 미디어를 활용한 학습방법으로 교육현장에서 학습자 참여를 촉진하고, 활동중심의 경험학습을 위한 학습방법으로 활용될 수 있을 것이다.

• 대한지구과학교육학회지
• /
• 제3권1호
• /
• pp.65-75
• /
• 2010

The purpose of this study is to present a Freedom Inquiry Method by Revised Science Curriculum in 2007. This study introduced IIM(Independent Inquiry Method), PBL(Problem Based Learning), Small Inquiry Method, Science Notebooks, Project Learning Method about Freedom Inquiry Method. The results of this study are as follows: First, IIM(Independent Inquiry Method) is studying method in the inquiry process center. The inquiry process is composed of total 9 phases, inquiry subject really it is, detailed aim deciding, information searching, it searches, quest result it arranges, aim evaluation, the report making, it announces, it evaluates, it is become accomplished. Second, It is a studying method which it starts with the problem which is Problem Based Learning, study atmosphere creation phase, problematic presentation phase and sleep static problem solving the phase which it attempts, it is become accomplished with autonomous studying phase, coordinated studying and discussion studying phase, discussion resultant announcement studying phase, arrangement and evaluation. Third, Small Inquiry Method, Call it accomplishes the call grade of the students among ourselves 4~8 people degree where only the quest learning capability is similar within class. Also interaction and coordinated function of the members between it leads and the subject which is given in the group it cooperates and it solves with it is a quest method which arrives to aim of commonness. This method divides on a large scale in three parts, it becomes accomplished in programming phase, quest accomplishment and resultant announcement. Fourth, Science Notebooks learns a scientific contents and a scientific quest function and the possibility of decreasing in order to be, from the fact that the help which it understands. This planing, data searching, it searches, becomes accomplished with resultant arrangement, announcement and evaluation. Fifth, The Project Learning Method the studying person oneself studying contents, it establishes a plan and it collects it accomplishes process of etc. it evaluates it leads and a subject and information and with real life it is a method which it studies naturally from the learning environment inside which is similar. This is preliminary phase, project start, project activity and project arrangement.

• International Journal of Contents
• /
• 제10권3호
• /
• pp.55-63
• /
• 2014

Existing science education that excludes narrative thinking impedes the understanding of the context of workbook content. The object of this research is to develop a storytelling-learning program based on narrative thinking to elevate learners' interest in science and expand their inventive problem-solving abilities. Following an analysis of the current Korean curriculum, eight types of storytelling materials that utilize local content were developed for grades 7-9. The learning program used quest storytelling and was designed such that learning activities such as investigation, discussion, and experimentation were included in the process of solving each quest. Learners experienced an interest in storytelling learning resulting from participation in this storytelling-learning program. Moreover, learners demonstrated inventive problem-solving abilities in the process of completing the stories. During the process of assembling the storytelling materials, the students interacted with enthusiasm and generated ideas. The teachers indicated a positive feedback to the storytelling program as a new attempt to stimulate learners' interests. In the future, with continuous development and application, storytelling-science-learning programs that base science learning on narrative thinking are expected to be successful.

• 한국콘텐츠학회논문지
• /
• 제19권2호
• /
• pp.88-98
• /
• 2019

2018년부터 2015 개정 교육과정이 적용되면서 과학 분야에서 학생들의 과학적 소양 함양을 위한 노력들이 이루어지고 있다. 이 중에서 과학실험은 학생들의 과학에 대한 학습 흥미 진작과 과학적 태도 함양에 도움을 준다. 과학적 사실을 이론으로 배우는 것보다 실험을 통해 배우게 되면 학습자의 이해가 높아지며 좀 더 오래 기억할 수 있다. 따라서 과학적 소양 함양에 있어서 실험을 매우 중요하다. 하지만 중학교에서는 시간과 예산, 재료 부족으로 과학실험이 순조롭게 진행되지 않고 있다. 본 연구에서는 이와 같은 문제점을 해소하고자 중학교 과학 교과서를 분석하고 과학 예비교사 학생들을 주로 대상으로 설문을 실시하여 가장 중요하게 여기는 과학실험을 선정한 후, 가상 환경에서 과학실험을 체험할 수 있도록 시뮬레이션형 모바일 앱을 개발하였다. 개발한 앱은 학습 강화 효과를 얻을 수 있도록 퀘스트 기반 학습방법을 사용하고 유니티를 사용하여 방탈출 게임형식을 접목하여 개발되었다. 본 연구에서는 앱을 개발한 후, 기존 앱들과의 차이점 분석과 사용자 피드백을 통해 향후 STEAM 콘텐츠로서 개발 의미와 기대효과에 대해 제언한다.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.31-40
• /
• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 한국게임학회 논문지
• /
• 제13권3호
• /
• pp.141-152
• /
• 2013

본 연구의 목적은 스마트 기기를 활용한 교수-학습 설계와 학습방법에 대한 새로운 접근 및 방향을 제안하는데 있으며, 첨단 매체를 활용하여 학습자 참여를 촉진하는 게임형태의 주제중심 통합학습 교수설계의 사례를 제시하였다. QR코드를 활용한 주제중심 통합학습의 사례인 퀘스트 기반학습은 학습동기, 경험학습, 사회적 상호작용을 촉진하는 역할을 한다. QR코드를 활용한 설계는 자신의 인지구조를 확장하는데 요구되는 사회적 상호작용 전략, 동기증진 전략, 구성 주의에서 강조하는 복잡한 문제 상황과 스캐폴딩 전략이 포함된 학습자 중심 학습 환경을 지원하였다. 스마트 기기를 이용한 게임형 퀘스트 기반학습을 통해 학습에 대한 흥미 증진, 학습자 중심의 참여 학습, 활동중심, 협력학습, 사회적 상호작용을 통한 지식의 창출 측면에 대한 교수 설계에 창의적인 학습 환경을 제공하는 기회를 확인하였다.

• International Journal of Computer Science & Network Security
• /
• 제22권12호
• /
• pp.185-196
• /
• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• 융합정보논문지
• /
• 제10권6호
• /
• pp.147-155
• /
• 2020

이 연구는 언어를 학습하는데 있어서의 학습자의 성격특성이 어떤 역할을 하여 어떤 관계를 가지고 있는지를 알아보는데 그 목적이 있다. 이를 위해서 기존의 연구를 분석하여 제시하였다. 즉, 최근의 연구들을 검토하기 위해서 언어학습과 성격특성에 관련된 연구들을 ProQuest, Google Scholar, 그리고 EBSCO 등의 데이터베이스를 통하여 추출하였다. 추출된 연구들을 검토하고 선정 및 정리하여 언어학습의 영역 즉 쓰기, 듣기, 말하기의 영역에서 성격특성이 어떤 역할을 하고 있는지를 문헌별로 정리하고 그 의미를 도출하였다. 문헌연구의 결과 외향성 및 개방성의 특성은 언어의 학습을 성공적으로 이끄는 데에 기여를 하고 있음을 확인하였다. 또한 외향성은 말하기에, 개방성은 듣기에 연관되어 있음을 연구들은 보고하고 있었다. 또한 학습자의 성격특성은 개인의 언어학습의 전략에도 관계되어 있고 전략수립에 영향을 미치고 있음이 확인되었다. 이같은 결과를 바탕으로 앞으로의 연구 방향에 대한 제언을 하였다.

• 대한안전경영과학회지
• /
• 제2권3호
• /
• pp.171-184
• /
• 2000

We may be given a set of observations with the classes or clusters. The aim of this article is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets. In this paper, machine learning algorithm classifiers based on CART, C4.5, CAL5, FACT, QUEST and statistical discriminant analysis are compared on various datasets in classification error rate and algorithms.

• International journal of advanced smart convergence
• /
• 제13권2호
• /
• pp.61-68
• /
• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.