• Education of Primary School Mathematics
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• v.23 no.2
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• pp.73-85
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• 2020

The purpose of this study was to examine the influence of the auxiliary questions of word problems presented to students on their problem solving-strategies and mathematical thinking and to discuss the educational implications of the results. As a result of making an analysis, problems that included auxiliary questions to give information on workable problem-solving strategies made it more possible for students of different levels to do relatively equal mathematical thinking than problems that didn't by inducing them to adopt efficient problem-solving strategies. And they were helpful for the students in the middle and lower tiers to find a clue for problem solving without giving up. But it's unclear whether the problems that provided possible strategies through the auxiliary questions stirred up the analogical thinking of the students. In addition, due to the impact of the problems provided, some students failed to adopt a strategy that they could have come up with on their own. On the contrary, when the students solved word problems that just offered basic recommendation by minimizing auxiliary questions, the upper-tiered students could devise various strategies, but in the case of the students in the middle and lower tiers, those who gave up easily or who couldn't find an answer were relatively larger in number.

• The Mathematical Education
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• v.50 no.3
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• pp.337-354
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• 2011

The purpose of the study was to investigate how students understand multiplication and division of fractions and how their understanding influences the solutions of fractional word problems. Thirteen students from 5th to 6th grades were involved in the study. Students' understanding of operations with fractions was categorized into "a part of the parts", "multiplicative comparison", "equal groups", "area of a rectangular", and "computational procedures of fractional multiplication (e.g., multiply the numerators and denominators separately)" for multiplications, and "sharing", "measuring", "multiplicative inverse", and "computational procedures of fractional division (e.g., multiply by the reciprocal)" for divisions. Most students understood multiplications as a situation of multiplicative comparison, and divisions as a situation of measuring. In addition, some students understood operations of fractions as computational procedures without associating these operations with the particular situations (e.g., equal groups, sharing). Most students tended to solve the word problems based on their semantic structure of these operations. Students with the same understanding of multiplication and division of fractions showed some commonalities during solving word problems. Particularly, some students who understood operations on fractions as computational procedures without assigning meanings could not solve word problems with fractions successfully compared to other students.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.295-304
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• 2005

This paper presents the problem of exhaustive test generation for detection of coupling faults between cells in word-oriented memories. According to this fault model, contents of any w-bit memory word in a memory with n words, or ability tochange this contents, is influenced by the contents of any other s-1 words in the memory. A near optimal iterative method for construction of test patterns is proposed The systematic structure of the proposed test results in simple BIST implementations.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.128-136
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• 2016

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

• Korean Journal of Adult Nursing
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• v.18 no.3
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• pp.468-478
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• 2006

Purpose: The purpose of this study were to find out the degree of memory decline and to confirm its correlated factors in old adults. Method: The subjects consisted of 68 old adults over the age 65 who living in Busan. Data were collected by the interview method, using a structured questionnaire and the testing method on the memory performance. Results: The old adults' memory performances declined in tasks of immediately word recall, delayed word recall, and face recognition and increased slightly in word recognition over 2 years. However, there was only significant difference in delayed word recall task. The significant variables to predict memory decline were age, literacy, depression, locus, and strategy. Conclusion: The memory decline of old adults wasn't more serious problem than the perceived one. There needs to be some intervention programs to prevent memory decline for the elderly.

• Journal of Educational Research in Mathematics
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• v.12 no.1
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• pp.17-27
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• 2002

To solve the addition word problems provides young children the chance to learn about and exercise in problem solving. This paper focuses on two aspects to be considered in addition word problems: the homogeneity of objects and the variety of contexts. The homogeneity of objects involved in addition word problems has to be kept in the following reasons: concept of unit, effectiveness of information, prevention of inappropriate variety, inconsistency of mathematics with real world, continuity between elementary and secondary mathematics. And for the variety of contexts, the additive structure proposed by G. Vergnaud, can be considered: composition, transformation, relation of comparison, composition of two transformations, composition of two relations, transformation of a relation. According to this structure, some examples, which contain homogeneous objects, were extracted from the elementary school mathematics textbooks.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.106-116
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• 2021

To increase the quality of the system in the subjective-type question grading and document classification, we need the paragraph detection. But it is not easy because it is accompanied by semantic analysis. Many researches on the paragraph detection solve the detection problem using the word based clustering method. However, the word based method can not use the order and dependency relation between words. This paper suggests the paragraph detection system using syntactic-semantic relation between words with the Korean syntactic-semantic analysis. This system is the hybrid system of word based, concept based, and syntactic-semantic tree based detection. The experiment result of the system shows it has the better result than the word based system. This system will be utilized in Korean subjective question grading and document classification.

• Phonetics and Speech Sciences
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• v.14 no.1
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• pp.29-36
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• 2022

The English word-final /rl/ cluster poses a particular problem for Korean learners of English because it is the sequence of two sounds, /r/ and /l/, which are not contrastive in Korean. This study compared the similarity distances between English and Korean speakers' /rl/ productions using the dynamic time warping (DTW) algorithm. The words with /rl/ (pearl, world) and without /rl/ (bird, word) were recorded by four English speakers and four Korean speakers, and compared pairwise. The F2-F1 trajectories, the acoustic correlate of velarized /l/, and F3 trajectories, the acoustic correlate of /r/, were examined. Formant analysis showed that English speakers lowered F2-F1 values toward the end of a word, unlike Korean speakers, suggesting the absence of /l/ in Korean speakers. In contrast, there was no significant difference in F3 values. Mixed-effects regression analyses of the DTW distances revealed that Korean speakers produced /r/ similarly to English speakers but failed to produce the velarized /l/ in /rl/ clusters.

• Journal of Information Management
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• v.41 no.2
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• pp.31-46
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• 2010

Now named-entity recognition(NER) as a part of information extraction has been used in the fields of information retrieval as well as question-answering systems. Unlike words, named-entities(NEs) are generated and changed steadily in documents on the Web, newspapers, and so on. The NE generation causes an unknown word problem and makes many application systems with NER difficult. In order to alleviate this problem, this paper proposes a new feature generation method for machine learning-based NER. In general features in machine learning-based NER are related with words, but entities in named-entity dictionaries are related to phrases. So the entities are not able to be directly used as features of the NER systems. This paper proposes an encoding scheme as a feature generation method which converts phrase entities into features of word units. Futhermore, due to this scheme, entities with semantic information in WordNet can be converted into features of the NER systems. Through our experiments we have shown that the performance is increased by about 6% of F1 score and the errors is reduced by about 38%.