• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.47-55
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• 2009

The purpose of this study is to ermine the effect of digital storytelling as a strategy of programming education to improve students' learning motivation and achievement. To overcome the difficulty of programming education in elementary school and find teaching method which derives the students' motivation, we used a digital storytelling in programming class. Digital storytelling that is considered as an important factor of edutainment gives interest to learners with interaction and stories for programming materials. The result is that elementary school students are more interested in programming and attend actively and their motivation and achievement is improved. Therefore it can gives elementary school students a positive experience with programming that will hopefully contribute to a more positive attitude towards computer science.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.541-550
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• 2009

This research attempted to find out any implications for strategies to design and develop the connections between the activities of the brain function and the fields of English learning (dictation, word level, speaking, word memory, listening). Thus, in developing the brain based learning model for the English education, attempts need to be made to help learners to keep the whole brain toward learning. On this point, this study indicated the significant results for the exclusive brain location and the brainwaves on the each English learning field by the quantitative EEG analysis. The results of this study presented the guidelines for the balanced development of the left brain and the right brain to train the specific site of the brain connected to the English learning fields. In addition, whole brain training model is developed by the quantitative EEG data not by the theoretical learning methods focused on the right brain training.

• The Journal of Korean Association of Computer Education
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• v.10 no.5
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• pp.11-20
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• 2007

In order to measure students' fun and the effectiveness of teaching materials related to computer science theory, this paper conducted an experiment lesson against middle school students using Timbel's 'Unplugged' textbook. A preliminary test was conducted and it revealed that the students' understanding of computer science theory was extremely poor. The learner's fun for the experimental lesson showed the positive reaction in the viewpoint of expectation, engagement, endurability. In post-test after one month, the students were able to recall most of the materials covered during the experiment lesson. In addition, they all showed sign of good understanding. In conclusion, Timbel's textbook proved effective in increasing students' fun and learning ability related to computer science theory. Furthermore, this paper would like to suggest a new type of Informatics science teaching material that might be essential for future computer science education.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.797-802
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• 2023

In an environment where English is a foreign language, English learners can use AI voice chatbots in English-speaking practice activities to enhance their speaking motivation, provide opportunities for communication practice, and improve their English speaking ability. In this study, we propose a teaching-style AI voice chatbot that can be easily utilized by lower elementary school students and enhance their learning. To apply the Teachable Agent system to language learning, which is an activity based on tense, context, and memory, we proposed a new method of TA by applying the Teachable Agent to reflect the learner's English pronunciation and level and generate the agent's answers according to the learner's errors and implemented a Teachable Agent AI chatbot prototype. We conducted usability evaluations with actual elementary English teachers and elementary school students to demonstrate learning effects. The results of this study can be applied to motivate students who are not interested in learning or elementary school students to voluntarily participate in learning through role-switching.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.49-50
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• 2016

본 논문에서는 Kinect 센서 기반의 카드 매칭 게임을 설계하고 구현한다. 이 게임은 유아용 카드 매칭 게임으로 카드 뒷면을 화면에 배치시키고 무작위로 앞면의 그림을 보여준다. 플레이어는 앞면의 그림을 위치별로 기억하여 같은 짝의 그림을 선택하여 맞춘다. 다른 짝을 매칭 할 경우 다시 뒷면으로 뒤집히고 같은 짝을 매칭 할 경우는 해당 그림의 영어 단어를 팝업시킨다. 예를 들어 토끼 그림의 짝을 매칭 할 경우 RABBIT이란 단어를 팝업 시킨다. 그리고 플레이어는 RABBIT이란 단어를 발음하면 Kinect는 음성을 인식하여 팝업된 창을 종료하고, 게임을 계속 진행한다. 게임은 화면에 배치시키는 카드를 $2{\times}2$부터 시작하여 난이도 별로 증가 시키고 스테이지 별로 콘텐츠를 나눠 영유아들이 재미있게 게임을 즐기면서 영어 단어를 학습할 수 있는 게임이다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1563-1570
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• 1999

In this paper, we proposed the FPA(Fixed Partition Averaging) algorithm in order to improve the storage requirement and classification time of Memory Based Reasoning method. The proposed method enables us to use the storage more efficiently by extracting representatives out of training patterns. After partitioning the pattern space into a fixed number of equally-sized hyperrectangles, it averages patterns in each hyperrectangle to extract a representative. Also we have used the mutual information between the features and classes as weights for features to improve the classification performance.

• Journal of KIISE
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• v.44 no.11
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• pp.1125-1129
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• 2017

LSTM(Long Short-term Memory) is a kind of RNN(Recurrent Neural Network) in which a next-state is updated by remembering the previous states. The information of calling a sequence in a malware can be defined as system call function that is called at each time. In this paper, we use calling sequences of system calls in malware codes as input for malware classification to utilize the feature remembering previous states via LSTM. We run an experiment to show that our method can classify malware and measure accuracy by changing the length of system call sequences.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.53-59
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• 2002

This paper presents a model for diagnosing students'learning misconceptions in the domain of heat and temperature using fuzzy cognitive maps(FCM) and fuzzy associative memory(FAM). In a model for diagnosing learning misconceptions. an FCM can represent with cause and effect between preconceptions and misconceptions that students have about scientific phenomenon. An FAM which represents a neurallike memory for memorizing causal relationships is used to diagnose causes of misconceptions in learning. This study will present a new method for more autonomous and intelligent system than a model to diagnose misconceptions that was being done with classical methods in learning and may contribute as an intelligent tutoring system for learning diagnosis within various educational contexts.

• Management & Information Systems Review
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• v.35 no.2
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• pp.45-66
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• 2016

Firms can make scale of economy and scope of economy by internalizing and using new advanced technology and knowledge from technology cooperation network, decrease risk and cost with partner firm of technology cooperation network, and increase market advantage of product & strengthen firms' position in the market. Due to the advantages of technology cooperation network, the related studies have focused on the positive effect of technology cooperation network. However, the related studies investigating the relationship between technology cooperation network and firm performance have only examined the role of technology cooperation network. Safeguarding mechanism, relationship learning, and relationship performance are categorized into the process of technology cooperation network, and this categorization is applied as resources, capability, and performance by resource based view. The empirical results are reported as belows. First, relationship specific investment and relationship capital positively affect on relationship learning as capability. Second, information sharing, common information understanding, and relationship specific memory development positively affect on long-term orientation, but information sharing has no impact on efficiency and effectiveness. Third, relationship specific investment positively affects on relationship capital and efficiency and effectiveness have positive effects on long-term orientation. Applying technology cooperation network in asymmetric technology dependency with resource based theory, this study suggested the importance of both safeguarding and relationship learning by investigating the relationship among safeguarding, relationship learning, and relationship performance. And it is worthy that this study investigated how firms' behavior change affects relationship performance in the relationship of technology cooperation partner.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.478-487
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• 2004

We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.