• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.71-78
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• 2020

To maximize educational effect with new educational methods in the 4th Industrial Revolution era, immersive education has become the core type of education and virtual reality (VR) is at the center of realistic content. VR education is increasing in school, but researches on VR production education are insufficient. Our study has proposed a school-level curriculum for students to create their own VR content. The output and the survey results were analyzed to find out the learner characteristics of elementary school students, middle school students, and pre-service teachers at each school level. As a result, there were some noticeable differences in concentration, content subject, and production time according to school level. Primary school students focused on their subjects, middle school students related to learning, and pre-service teachers put top priority on contents useful for primary education.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1101-1107
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• 2017

This study explored the effect of ASR-based pronunciation instruction, using a mobile platform, on EFL learners' pronunciation development. Particularly, this quasi-experimental study focused on whether using mobile ASR, which provides voice-to-text feedback, can enhance the perception and production of target English consonants minimal pairs (V-B, R-L, and G-Z) of Korean EFL learners. Three intact classes of 117 Korean university students were assigned to three groups: a) ASR Group: ASR-based pronunciation instruction providing textual feedback by the mobile ASR; b) Conventional Group: conventional face-to-face pronunciation instruction providing individual oral feedback by the instructor; and the c) Hybrid Group: ASR-based pronunciation instruction plus conventional pronunciation instruction. The ANCOVA results showed that the adjusted mean score for pronunciation production post-test on the Hybrid instruction group (M=82.71, SD =3.3) was significantly higher than the Conventional group (M=62.6, SD =4.05) (p<.05).

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.332-344
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• 2016

This study aimed at investigating the effects of grouping participants in pairs according to their relative vocabulary proficiency on the incidental improvement of vocabulary knowledge. Forty six university students were divided into three groups (high-high(n=14), high-low(n=18), or low-low(n=14)) and took part in the study. They performed three vocabulary activities in pairs as extra-class works. Data were collected from one receptive vocabulary knowledge test scores before treatment and two posttest scores after treatment. The results showed that, unlike former study results, HL dyads acquired more vocabulary receptively and productively than HH or LL dyads did, which demonstrated that collaborative pair activity was conducive to the growth of vocabulary knowledge. Furthermore, not only higher proficiency participants in HL pairs made greater vocabulary gains than participants in HH pairs did but also lower proficiency participants gained more vocabulary than participants in LL pairs did. Based on these results, we discussed pedagogical implications.

• Journal of the Korea Convergence Society
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• v.8 no.2
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• pp.269-276
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• 2017

The purpose of this study is to review empirical research on Form-focused instruction (FFI) in Korean language education from a critical perspective to better understand the effectiveness of FFI. To achieve this goal, several databases were searched to locate relevant experimental and quasi experimental studies published in peer-reviewed journals. Out of 66 studies collected, 12 studies met the inclusion criteria. The studies were then analyzed in terms of subjects, target grammar, treatment, measurement, and the learning outcomes of different techniques. In general, several types of FFI techniques had positive effects on helping learners acquire Korean as a second or foreign language. The results of the study will provide a conceptual framework which identifies the major factors affecting the effectiveness of FFI. The results will also be able to inform future meta-analytical research of existing studies.

• Journal of The Korean Association of Information Education
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• v.17 no.2
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• pp.225-232
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• 2013

The smart education is performed in the education field, but the contents related to the subject class performed in the various smart devices are insufficient and it is necessary to continue training teacher and learner for the app development education. Therefore this study developed and applied the contents production education program of the game type web app around elementary science 6 grade 1 term utilizing Storyline authoring tools, similar to PowerPoint and be executable in the various devices. After educating students, we investigate the convenience of the use of Storyline, satisfaction level of learning contents work activity, and the continued availability, and etc,. They are interested in the activity of the smart educational contents development and they wished to participate in the continued app development education and activity in spite of it make difficulty for using English menu.

• Journal of Yeungnam Medical Science
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• v.34 no.1
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• pp.62-68
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• 2017

Background: This study was conducted to examine the academic achievements of first year medical students in one medical school based on their characteristics and student selection factors of admission. Methods: The admission scores of student selection factors (Medical Education Eligibility Test [MEET], grade point average [GPA], English test score and interview) and demographic information were obtained from 61 students who had interviewed (multiple mini interview [MMI]) for admission (38 graduate medical school students in 2014, 23 medical college-transfer students in 2015). T-tests and ANOVA were used to examine the differences in academic achievement according to the student characteristics. Correlations between admission criteria scores and academic achievements were examined. Results: MEET score was higher among graduate medical students than medical college transfer students among student selection factors for admission. There were no significant differences in academic achievement of first grade medical school between age, gender, region of high school, years after graduation and school system. The lowest interview score group showed significantly lower achievement in problem-based learning (PBL) (p=0.034). Undergraduate GPA score was positively correlated with first grade total score (r=0.446, p=0.001) among admission scores of student selection factors. Conclusion: Students with higher GPA scores tend to do better academically in their first year of medical school. In case of interview, academic achievement did not lead to differences except for PBL.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.435-439
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• 2007

Document categorization system is important in the internet age in which huge number of documents are created and need to be dealt with. By this reason a lot of research has been done in this field. For the development of the system, a supervised learning method is widely used. This approach needs a test collection as a prerequisite. For the case of English, several test collections are available which provide a lot of help for developing systems and doing research. But no public test collections have been reported and are not available in the case of Korean. To improve the situation for Korean we are undergoing the construction of a Korean test collection. In this paper the approaches being used and current stage of the collection will be described.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.297-304
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• 2010

Clustering method which based on sentence type or document genre is a technique used to improve translation quality of SMT(statistical machine translation) by domain-specific translation. But there is no previous research using sentence type and document genre information simultaneously. In this paper, we suggest an integrated clustering method that classifying sentence type by syntactic structure similarity and document genre by word similarity information. We interpolated domain-specific models from clusters with general models to improve translation quality of SMT system. Kernel function and cosine measures are applied to calculate structural similarity and word similarity. With these similarities, we used machine learning algorithms similar to K-means to clustering. In Japanese-English patent translation corpus, we got 2.5% point relative improvements of translation quality at optimal case.

• Journal of the Korean Chemical Society
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• v.54 no.4
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• pp.465-470
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• 2010

Using interesting video clips corresponding to lesson subjects for students who favour visual representation is one of the good methods to enhance students' preference for science class. There are many moving picture web sites to get video clips easily via internet and 'YouTube' is very popular and one of the largest reservoir. In this study, every student in the 'Chemistry I' class, which is a class for 11th grade, was requested to search a video clip corresponding to lesson subjects and to make a presentation in the class. After 1st semester, students' response about the class using YouTube was examined by survey. As a result, students preferred and were interested in the class using YouTube than class centered on textbook. And students preferred YouTube clips showing unusual experiments that were related with contents of subject. In addition, experiments and watching their real phenomena were an interesting factor and helpful factor of learning chemistry in YouTube video clips, respectively. However, translation of English used in the video clips seemed to be a difficult part for students.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.55-63
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• 2020

Voice conversion can be applied to various voice processing applications. It can also play an important role in data augmentation for speech recognition. The conventional method uses the architecture of voice conversion with speech synthesis, with Mel filter bank as the main parameter. Mel filter bank is well-suited for quick computation of neural networks but cannot be converted into a high-quality waveform without the aid of a vocoder. Further, it is not effective in terms of obtaining data for speech recognition. In this paper, we focus on performing voice-to-voice conversion using only the raw spectrum. We propose a deep learning model based on the transformer network, which quickly learns the voice conversion properties using an attention mechanism between source and target spectral components. The experiments were performed on TIDIGITS data, a series of numbers spoken by an English speaker. The conversion voices were evaluated for naturalness and similarity using mean opinion score (MOS) obtained from 30 participants. Our final results yielded 3.52±0.22 for naturalness and 3.89±0.19 for similarity.