• Journal of Korea Game Society
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• v.11 no.5
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• pp.79-88
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• 2011

The purpose of this study is to confirm meaning for quest-based learning designed as one of learning methods with new media, which the contents were made of QR code based on smart-phone. For this purpose, this study conducted 8 times of class during one month with 32 elementary third graders. And analysis for the result of this study was carried out MANOVA focus on four sub-factor of motivation. The results showed positive effect for quest-based learning in attention and confidence factor on motivation. Quest-based learning of gaming version as a learning method using various media can use a learning method for facilitation of learner participation, supporting of learning by doing.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.65-75
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• 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
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• v.10 no.3
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• pp.55-63
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• 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.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.88-98
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• 2019

As the 2015 revised curriculum is being implemented from 2018, efforts are being made to cultivate scientific literacy among students in the field of science. Scientific experiments help students to develop their interest in Science and their scientific attitudes. Learning through experimentation rather than learning scientific facts increases learners' understanding, and can be remembered longer. Therefore, experiments in Science subject are very important. However, in middle schools, scientific experiments are not performed due to the lack of time, budget and experimental material. In this research, we analyze middle school science textbooks, conduct questionnaires for students of science pre-service teachers, select the most important science experiments, and develop a mobile App to simulate and experience scientific experiments with the App. The proposed App is developed in a game format using quest-based learning methods to gain learning enhancement. It is also made using Unity. In this paper, after developing the app, we propose the direction of STEAM contents development through analyzing the difference from existing apps and the feedback from users.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 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.

• Journal of Korea Game Society
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• v.13 no.3
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• pp.141-152
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• 2013

This research aims at suggesting a case of designing theme-based integrated learning for usage smart media. New approach and direction for developing a gaming instructional design was suggested which can encourage learners to participate in. Quest-based learning, the learning environment design where learners conduct various learner-centered activities, plays an important role of reinforcing the motivation, promoting experiential and cooperative learning based on social interaction. The design using QR codes has been proved to be able to offer the learner-centered learning environment which includes social interaction strategy required for learners expanding their cognitive structure, motivation enhancing strategy encouraging their consistent participation in learning, complex problematic situation and scaffolding strategy emphasized by constructivism. And it is expected to contribute to promoting the design of theme-based integrated learning which is being demanded in the educational environment recently by combining systematic design process and strategies.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 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.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.147-155
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• 2020

This is a literature review study on personality traits' role in language learning. Personality traits play an important role in language learning. In order to review research outcomes in recent studies, articles related to language learning and personality traits were collected through research databases such as ProQuest, Google Scholar, and EBSCO. Based on the analysis of collected literature, this study revealed that extraversion and openness to experience are the personality traits leading to the successful language learning. More specifically, extraversion was related to speaking skills while openness to experience was related to listening. It is also important to note which learning strategies are more likely to be utilized in second language learning and personality traits that are more likely to use them. These findings focus on writing skills, listening skills, and speaking skills. Further studies in the field are suggested.

• Journal of the Korea Safety Management & Science
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• v.2 no.3
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• pp.171-184
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• 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
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• v.13 no.2
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• pp.61-68
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• 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.