• The Journal of Korean Association of Computer Education
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• v.21 no.4
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• pp.1-10
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• 2018

Block programming environment, which is represented by Scratch in elementary and middle school programming education, is suitable for learner's characteristics and cognitive level, and is recommended not only for beginners. Transference to the text programming environment after the block programming is essential for understanding the data processing process, understanding the accuracy and efficiency aspects of algorithms, and creating SW activity. In addition, it is presented step by step in the programming curriculum. In this study, developed WithBlock the online evaluation system for the purpose of transference from a block programming to a text programming environment. The developed system can solve the same algorithm problem in both block and text programming environment, and it can be used for elementary and secondary programming education by automatically scoring the written code and providing immediate feedback. In order to applicable to programming education in elementary and secondary surveyed the usability, learning possibility, interest and satisfaction of WithBlock. The results of the survey showed that it can be used for programming education.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.947-960
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• 2021

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner's responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.211-219
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• 2005

Several problems occur in reading education in a elementary school. They are the lack of considering the difference of student's capability and diagnosing their reading ability, the review activity focusing on only text and the discontinuation between school and home. Our system is designed and implemented to support leveled reading education using multimedia technology. We can diagnose the student's reading ability exactly and promptly and introduce suitable books considering their ability, and work the proper review activities with this system. It can help the students to interest their reading.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.205-210
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• 2016

인간은 문서전체를 읽지 않고 대표적인 단어를 보는 것만으로 정치나 스포츠 등의 분야를 정확히 인지할 수 있다. 문서 전체는 물론 부분 텍스트(단락)에 출현하는 소수의 단어 정보에서 문서의 분야를 정확히 결정하기 위한 분야연상어의 구축은 중요한 연구과제이다. 미리 분야체계를 정의하고, 각 분야에 해당하는 문서를 인터넷이나 서적을 통해 수집한다. 본 논문은 수집 문서의 분야를 정확히 지시하는 분야연상어를 수집하는 방법을 제안한다. 문서의 분야결정 시점을 고려하여 분야연상어의 수준을 정하였다. 인도네시아어의 분야연상어 사전을 자동으로 구축하기 위해 먼저 한국어로 구축한 분야 연상 지식을 추출하는 방법을 제안한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.736-740
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• 2001

정보통신 분야의 혁신적인 기술개발로 최근 인터넷 기술을 이용한 다양한 분야에서 응용이 활발해지고 있다. 이러한 환경의 변화는 인터넷 서비스의 하나인 웹의 사용과 함께 교육분야에도 원격, 가상교육이라는 대안적 교육 체제를 탄생하게 하였다. 웹 이전의 교육용 시스템들은 텍스트나 간단한 그래픽 기반의 단방향의 학습과 평가가 가능했었다면, 웹의 분산 환경 제공으로 양방향의 상호 작용적 학습이 가능해졌을 뿐만 아니라 실시간 학습과 평가를 가능하게 해 주었다. 본 논문에서는 웹 기반 문제은행 시스템의 장점을 이용하여 학습자의 평가 정보를 시스템이 분석하고 학습자의 수준에 맞는 문제를 제공함으로써 수준별 학습이 가능하도록 하였을 뿐만 아니라 학습자에게 가장 많이 틀린 단원의 문제에 대해서 보충학습의 기회를 제공함으로써 학습효과를 극대화할 수 있는 시스템을 설계하였다.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.205-210
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• 2016

인간은 문서전체를 읽지 않고 대표적인 단어를 보는 것만으로 정치나 스포츠 등의 분야를 정확히 인지할 수 있다. 문서 전체는 물론 부분 텍스트(단락)에 출현하는 소수의 단어 정보에서 문서의 분야를 정확히 결정하기 위한 분야연상어의 구축은 중요한 연구과제이다. 미리 분야체계를 정의하고, 각 분야에 해당하는 문서를 인터넷이나 서적을 통해 수집한다. 본 논문은 수집 문서의 분야를 정확히 지시하는 분야연상어를 수집하는 방법을 제안한다. 문서의 분야결정 시점을 고려하여 분야연상어의 수준을 정하였다. 인도네시아어의 분야연상어 사전을 자동으로 구축하기 위해 먼저 한국어로 구축한 분야 연상 지식을 추출하는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.163-166
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• 2001

현재의 무선 인터넷 기술은 하루가 다르게 빠른 발전을 하고 있다. 하지만 적은 메모리와 낮은 수준의 처리율을 가지고 있는 제한적 환경인 모바일 특성에 의해 대부분 텍스트 기반의 서비스만이 주류를 이루고 있다. 그러나 사용자들은 다양한 정보와 형태를 원하고 있다. 따라서 본 논문에서는 비교적 적은 메모리 사이즈와 낮은 수준의 처리율을 가진 단말기, 즉 제한된 모바일 환경에서도 다양한 정보 및 멀티미디어 서비스와 가상현실을 구현하고자 한다. 본 논문에서는 워드 서비스와 가상현실 서비스 두 가지를 제안한다. 워드 서비스는 일반 WAP 푸시 개념이 아닌 서버와 사용자간의 정보 교환을 위한 서비스 개념이다. 일반 컴퓨터에서 작업한 워드파일을 모바일에서도 작업을 할 수 있도록 한 서비스이고, 가상현실 서비스는 일반 언어의 3D 개념으로 디자인되어진 것이 아닌, 2D 의 이미지를 3D 화 시켜 모바일에서 실시간으로 사용자들이 서비스를 받을 수 있도록 제안하였다.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.