• School Mathematics
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• v.13 no.1
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• pp.155-173
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• 2011

Problem representation is a key aspect in solving word problems. The purpose of this study was to investigate the effects of instructional program based on visual schema representing five types of word problems(Marshall, 1995). Two second grade classes of an elementary school located in Seoul were participated in this study. In experimental class, an instructional program including schema tools were suggested and administered and the other comparison group did have regular classes using diagrams and tables. Pre and post test including 15 word problems each were utilized to test students' problem solving ability. In addition, test scores on students' language ability were used to control the effects of word comprehension level on problem solving. The result revealed that experimental group showed higher problem representation and solving scores after controling the effects of pre-test. In addition, there was significant positive correlation between the ability to apply exact problem schema and problem solving results. The correlation was .58. This study showed even in the early developmental stage young students can get benefits from having instructions of word problem schema.

• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.143-150
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• 2012

Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.

• 한국디지털정책학회:학술대회논문집
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• 2006.12a
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• pp.447-453
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• 2006

In this paper the contention that a case study on the e-learning contents by students' levels. The Key word of the future e-learning contents are student-centered education that considers each student's ability, aptitude, and career choice. The major way to realize this student-centered education is to implement differentiated curriculum by students' levels. Especially, in the off-line class, this method Is very difficult because if superior and inferior classes are established, those who are placed in the inferior class will be hurt, but e-learning is realized that.

• English Language & Literature Teaching
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• v.18 no.2
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• pp.67-84
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• 2012

The present study investigated the effect of sentence translation as a production task on short-term and long-term retention of foreign vocabulary. 87 EFL university students at a beginning level, enrolled in reading class participated in the study. The study compared the performance of three groups on vocabulary recall: (1) Control group, (2) Translation group, and (3) Copy group. During the treatment sessions, translation group translated L1 sentences into English, while copy group simply copied given English sentences with each target word. Results of the immediate test were collected each week from week 2 to week 5 and analyzed by one-way ANOVA. Results revealed that regarding short-term vocabulary retention, participants in rote-copy condition outperformed those in translation group. Four weeks later a delayed test was administered to measure long-term vocabulary retention. In contrast, the results of two-way repeated measures ANOVA showed that long-term vocabulary retention of translation group was significantly greater than copy group. The findings suggest that although sentence translation is rather challenging to low-level learners, it may facilitate long-term retention of new vocabulary given the more elaborate and deeper processing the task entails.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.191-197
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• 2024

Software reuse gives the meaning for rapid software development and the quality of the software. Most of the Java components/libraries open-source are available only in Java Archive (JAR) file format. When a software design enters into the development process, the developer needs to select necessary JAR files manually via analyzing the given software architecture and related JAR files. This paper proposes an automated approach, JarBot, to suggest all the necessary JAR files for given software architecture in the development process. All related JAR files will be downloaded from the internet based on the extracted information from the given software architecture (class diagram). Class names, method names, and attribute names will be extracted from the downloaded JAR files and matched with the information extracted from the given software architecture to identify the most relevant JAR files. For the result and evaluation of the proposed system, 05 software design was developed for 05 well-completed software project from GitHub. The proposed system suggested more than 95% of the JAR files among expected JAR files for the given 05 software design. The result indicated that the proposed system is suggesting almost all the necessary JAR files.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.829-840
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• 2005

Word Sense Disambiguation(WSD) is one of the most difficult problem in Korean information processing. We propose a WSD model with the capability to filter semantic information using the specific characteristics in dictionary dictions, and nth added information, useful to sense determination, such as statistical, distance and case information. we propose a model, which can resolve the issues resulting from the scarcity of semantic information data based on the word hierarchy system (thesaurus) developed by Ulsan University's UOU Word Intelligent Network, a dictionary-based toxicological database. Among the WSD models elaborated by this study, the one using statistical information, distance and case information along with the thesaurus (hereinafter referred to as 'SDJ-X model') performed the best. In an experiment conducted on the sense-tagged corpus consisting of 1,500,000 eojeols, provided by the Sejong project, the SDJ-X model recorded improvements over the maximum frequency word sense determination (maximum frequency determination, MFC, accuracy baseline) of $18.87\%$ ($21.73\%$ for nouns and inter-eojeot distance weights by $10.49\%$ ($8.84\%$ for nouns, $11.51\%$ for verbs). Finally, the accuracy level of the SDJ-X model was higher than that recorded by the model using only statistical information, distance and case information, without the thesaurus by a margin of $6.12\%$ ($5.29\%$ for nouns, $6.64\%$ for verbs).

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.32-41
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• 2000

In this paper, we proposed a method which predict the verb by using the neural network in order to enhance communication rate in augmentative communication system for Korean language disorders. Each word is represented by an information vector according to syntax and semantics, and is positioned at the state space by being partitioned into various regions different from a dictionary-like lexicon. Conceptual similarity is realized through position in state space. When a symbol was pressed, we could find the word for the symbol at the position in the state space. In order to prevent verb prediction's redundancy according to input units, we predicted the verb after separating class using the neural network. In the result we can enhance $20\% communication rate in the restricted space

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.73-75
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• 2022

Scene Text Recognition is a technology used in the field of artificial intelligence that requires manless robot, automatic vehicles and human-computer interaction. Though scene text images are distorted by noise interference, such as illumination, low resolution and blurring. Unlike previous studies that recognized only English, this paper shows a strong recognition accuracy including various characters, English, Korean, special character and numbers. Instead of selecting only one class having the highest probability value, a candidate word can be generated by considering the probability value of the second rank as well, thus a method can be corrected an existing language misrecognition problem.

• Journal of Science Education
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• v.37 no.3
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• pp.573-582
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• 2013

This study was conducted to inquire the semantic structure with science terminology used in middle school science class, and based on this, we wanted to look for the way to increase effectiveness of science teaching. In this study, we extracted twenty-six science terminologies used in "Force and Motion" unit in middle school science textbook, and administered word association test using the 26 science terminologies to 316 middle school students. As the result, we found that students had a divergent semantic structure to given science terminology, and there were cases to be interpreted as different meaning with teacher's intention. Also, we identified the terminologies which were not familiar to middle school students. It was found that female students were more familiar with science termilology than male students, and there were differences between schools.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.600-608
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• 2017

Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.