• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.341-348
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• 2022

Formulaic sequences are continuous or discontinuous series of words that are seemingly treated like single units. Formulaic sequences play a key role in language development, and formulaic sequences acquisition determines the success or failure of language development. This study proposes a reading aloud activity as a way for international students to learn formulaic sequences. A class focused on reading aloud was conducted with 41 international students taking a general English course at a university in Seoul. For 15 weeks, video lectures and real-time Zoom classes were conducted in parallel. The animated film Frozen was used as course material. In the video lectures, the teacher interpreted the movie script in easy Korean and read aloud formulaic sequences. Students were tasked with reading the sentences with formulaic sequences aloud, recording themselves reading aloud, and submitting their recordings. During real-time class meetings, students performed the activity of reading aloud the formulaic sequences they had studied in the video lectures. There was a significant increase in the interpretation and sentence writing of formulaic sequences in participants' post-evaluation compared to the pre-evaluation. Through the study's survey, students exhibited positive views in the affective domains.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.404-411
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• 2008

The purpose of this study is to suggest the effects of schema activation on reading comprehension. The subject of a sample survey was a 36 student experimental group and a 32 student control group, total 68 students at third grade class of C Middle School in Gwangju. Students ability to read English in the two groups were almost the same through, which was shown by pre-test administered before the beginning of the experiment. As a pre-reading activity, the experimental group was showed the pictures and vocabularies related to the text before reading. The other control group did Grammar Translation Method about text. The data needed for this study was obtained by the questionnaires with 25 questions about the English reading. The data analyzing method was t-test through the statistics program SPSS 12.0. The result of this study is as follows : First, the experimental group got a more meaningful score than the control group at the test. Second, pre-reading activities for providing prior knowledge of the text were affected by the student's English proficiency, peculiarly more effective on low level student than advanced level. Studying English reading through schema activation led the students to be present in classes with interests, so the experimental group showed more academic accomplishments than the control group.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.267-272
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• 2021

This study's purpose is to investigate the effects of reading movie scripts aloud in non-face-to-face general English courses on international students' English ability in the COVID-19 era. A general English class was delivered once a week for 15 weeks to 47 international students at a Seoul-based university. The animated movie Tangled and its script were used as learning materials. Biweekly, students had to watch video lectures using the university's learning management system(LMS) and read scripts aloud through Zoom. In the video lectures, the teacher went over specific vocabulary and interpreted the movie scripts in easy Korean. For the second activity through Zoom, international students read the movie script aloud individually and in groups. The post-test revealed significant improvements in both reading and writing, as compared to the pre-test. Through the study's survey, participants exhibited positive attitudes in affective domains(understanding, satisfaction, interest, and recommendation).

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.141-148
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• 2021

The mainstream tool for software education for elementary students is Educational Programming Language. It is essential for upper graders to advance from EPL to text based programming language. However, many students experience difficulty in adopting to this change since Python is run in English. Python is an actively used TPL. This study focuses on developing an education program to facilitate learning Python for Korean speaking students. We have extracted the necessary reserved words needed for data analysis in Python. Then we replaced the extracted words into Korean terms that could be understood in elementary level. The replaced terms were matched on one-to-one correspondence with reserved words used in Python. This devised program would assist students in experiencing data analysis with Python. We expect that this education program will be applied effectively as a basic resource to learn TPL.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.344-351
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• 2016

Recently, mobile application for english vocabulary learning is being developed actively. However, most mobile English vocabulary learning applications did not effectively connected with the technical advantages of mobile learning. Also,the study of mobile english vocabulary learning app are still insufficient. Therefore, this paper development a english vocabulary context-learning Agent that can practice context learning more reasonably using a location-based service, a character recognition technology and augmented reality technology based on smart phones. In order to evaluate the performance of the proposed agent, we have measured the precision and usability. As results of experiments, the precision of learning vocabulary is 89% and 'Match between system and the real world', 'User control and freedom', 'Recognition rather than recall', 'Aesthetic and minimalist design' appeared to be respectively 3.91, 3.80, 3.85, 4.01 in evaluation of usability. It were obtained significant results.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.385-395
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• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.576-593
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• 2021

In this study, in order to investigate the factors that affect the percentage of correct answers and the degree of discrimination of the TOEIC test, a regression analysis was performed using corpus indicators that influence correct answer rate and the degree of discrimination for each part derived from the item analysis. The basic calculation word_length, consistency index LSA_overlap_adjacent_sentences, lexical diversity MTLD_VOCD, conjunction All_logical_causal_connectives_incidence, situational model casual_particles_causal_verbs_Ratio, syntactic complexity Left_embeddedness, and syntactic pattern density Infinitive_density were found to have negative effects. These factors that lower the correct answer rate can be utilized when setting learning goals. Vocabulary diversity index MTLD_VOCD, conjunction Additive_connectives_incidence, syntactic pattern density Infinitive_density, and lexical information person1_2_pronoun_incidence were found to have a positive effect. Factors influencing the increase in discrimination may provide important information for developing a learning program.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.426-436
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• 2022

This study aimed to explore the role of models and reformulations as feedback to English writing in L2 learners' noticing and their writing. 92 participants were placed into three groups; a models group (MG), a reformulations group (RG), a control group (CG), involved in a three-stage writing task. In stage 1, they were asked to perform a 1st draft of writing, while taking notes on the problems they experienced. In stage 2, the MG was asked to compare their writing with a model text and the RG with a reformulated version of it. They were instructed to write down whatever they noticed in their comparison. The CG was asked to just read their writing. In stage 3, all the participants attempted subsequent revisions. The results indicated that all the participants noticed problematic linguistic features the most in a lexical category, and models and reformulations led to higher rate of noticing the problematic linguistic features reported in stage 1 and contributed to subsequent revisions. It was also revealed that the MG and RG significantly improved with their writings of MG and RG on the post-writing test. The findings imply that models and reformulations result in better performance in L2 writing and should be promoted in an English writing class.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• Journal of KIISE
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• v.43 no.6
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• pp.653-661
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• 2016

The existing pattern-based triple generation systems based on distant supervision could be flawed by assumption of distant supervision. For resolving flaw from an excessive assumption, statistics information has been commonly used for measuring confidence of patterns in previous studies. In this study, we proposed a more accurate confidence measure based on semantic similarity between patterns and properties. Unsupervised learning method, word embedding and WordNet-based similarity measures were adopted for learning meaning of words and measuring semantic similarity. For resolving language discordance between patterns and properties, we adopted CCA for aligning bilingual word embedding models and a translation-based approach for a WordNet-based measure. The results of our experiments indicated that the accuracy of triples that are filtered by the semantic similarity-based confidence measure was 16% higher than that of the statistics-based approach. These results suggested that semantic similarity-based confidence measure is more effective than statistics-based approach for generating high quality triples.