• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.649-663
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• 2015

This study investigated the current state of writing in research papers of science education with focus on the translationese and basic Korean grammar, and found a way of improving the Korean language. The science education research have characteristics of both social science and natural science, and of having more quantitative than qualitative research, which could influence the writing of the research paper. The translationese means the conventional expression originated from foreign language other than Korean. The basic Korean grammar includes 'agreement,' 'spelling, word spacing, punctuation mark,' 'causative suffix,' 'use of English or loanword,' and the translationese is divided in 'English,' 'Japanese,' and 'English and Japanese.' The sentences in nine research papers in the 'Journal of the Korean Association for Science Education' were analyzed, and the problematic sentences were discussed and provided with alternatives. The cases with high frequency include '-jeok,' 'use of English,' 'expression of the plural,' 'passive voice of the verb with -hada,' '-go inneun,' '-eul tonghayeo,' '-e daehayeo,' 'gajida,' 'genitive case marker -eui,' 'passive voice with subject of thing,' and 'causative suffix, -sikida.' Based on the results, the characteristics of writing of science education research papers were described as 'writing of quantitative research,' 'objective writing of academic research,' and 'writing of research of foreign origin.' In order to improve the writing of research paper of science education, the science education researcher should pay attention to basic Korean grammar and the translationese, and be familiar with the concrete examples of problematic cases. The results of this study could be used in the education of writing and grammar of Korean language.

• 한국체육학회지인문사회과학편
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• v.54 no.3
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• pp.439-452
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• 2015

The purpose of this study is to encourage continuous participation in sports and to provide basic data for the promotion of participation in leisure sports. To achieve the purpose, this study conducted factor scaling analysis on participation motives for leisure sports and subdivided them to analyze psychological reactions of participants. As for study methods, this study collected master and doctor's degree theses and academic journals on motives for sports participation that were conducted from 1997 to 2012 from Korean major search engines. On the search engines, a keyword 'motive' was searched first and then studies on participation motive for leisure sports were collected. Key words that appeared when searching 'motive' were combined with other key words and word spacing between them were checked before conducting a literature analysis. The study results showed that participation motives for leisure sports were divided into a participation motive, an internal motive, an external motive, a leisure motive and other motives. It was identified that there were 23 factors for the participation motive, 17 factors each for the internal motive and the external motive, 8 factors for the leisure motive and 57 factors for other motives. It was found out that 76 factors were used to study a participation motive for leisure sports, excluding the factors that have similar or overlapping meaning based on each factor.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.455-463
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• 2010

In this paper, we present an approach to sentence boundary detection for web documents that builds on statistical-based methods and uses rule-based correction. The proposed system uses the classification model learned offline using a training set of human-labeled web documents. The web documents have many word-spacing errors and frequently no punctuation mark that indicates the end of sentence boundary. As sentence boundary candidates, the proposed method considers every Ending Eomis as well as punctuation marks. We optimize engine performance by selecting the best feature, the best training data, and the best classification algorithm. For evaluation, we made two test sets; Set1 consisting of articles and blog documents and Set2 of web community documents. We use F-measure to compare results on a large variety of tasks, Detecting only periods as sentence boundary, our basis engine showed 96.5% in Set1 and 56.7% in Set2. We improved our basis engine by adapting features and the boundary search algorithm. For the final evaluation, we compared our adaptation engine with our basis engine in Set2. As a result, the adaptation engine obtained improvements over the basis engine by 39.6%. We proved the effectiveness of the proposed method in sentence boundary detection.