• Journal of Engineering Education Research
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• v.11 no.2
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• pp.42-49
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• 2008

Though the 'Program Educational Objectives' is the first and important criterion in ABEEK's engineering education accreditation, exact meaning and implementation methods have not been understood well. It was often confused with 'Program Outcomes' and its implementation and evaluation methods do not reflected well on the concepts of "outcomes based and demand driven education". A new implementation model for 'Program Educational Objectives' has been developed using step by step application. The model explains the meaning of every step (phase), and key constituents in each phase. The specialization and CQI of the program could be satisfied by applying the model properly.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.341-347
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• 2017

As the amount of document data increases, the user needs summarized information to understand the document. However, existing document summary research methods rely on overly simple statistics, so there is insufficient research on multiple document summaries for ambiguity of sentences and meaningful sentence generation. In this paper, we investigate semantic connection and preprocessing process to process unnecessary information. Based on the vocabulary semantic pattern information, we propose a multi-document summarization method that enhances semantic connectivity between sentences using VAE. Using sentence word vectors, we reconstruct sentences after learning from compressed information and attribute discriminators generated as latent variables, and semantic connection processing generates a natural summary sentence. Comparing the proposed method with other document summarization methods showed a fine but improved performance, which proved that semantic sentence generation and connectivity can be increased. In the future, we will study how to extend semantic connections by experimenting with various attribute settings.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.

• Journal of KIISE
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• v.43 no.7
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• pp.773-780
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• 2016

Semantic information and features are very important for Semantic Role Labeling(SRL) though many SRL systems based on machine learning mainly adopt lexical and syntactic features. Previous SRL research based on semantic information is very few because using semantic information is very restricted. We proposed the SRL system which adopts semantic information, such as named entity, word sense disambiguation, filtering adjunct role based on sense, synonym cluster, frame extension based on synonym dictionary and joint rule of syntactic-semantic information, and modified verb-specific numbered roles, etc. According to our experimentations, the proposed present method outperforms those of lexical-syntactic based research works by about 3.77 (Korean Propbank) to 8.05 (Exobrain Corpus) F1-scores.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.21-32
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• 2021

The current study investigates how Malaysian ESL learners' L2 (English) speaking fluency is reflected in advanced L2 reading and what difficulties they encounter in reading comprehension. Nine Malaysian students attending a Korean university participated in qualitative research using in-depth and semi-structured interviews. The data revealed that L2 was a very familiar language, and their speaking fluency in L2 reduced the anxiety of L2 reading in general. However, it did not play a significant role in reading at an advanced level. Their difficulties in reading were mainly due to a lack of vocabulary knowledge. However, insufficient background knowledge and interest also frustrated their reading tasks. These factors lowered their reading comprehension, causing inaccurate interpretations or discouraging their endeavors to find messages from the given text. Thus, these findings should be carefully addressed in reading classes for Korean L2 learners as well as international students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.24-29
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• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• 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).

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.589-596
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• 2022

This study aims to investigate the effect of an educational gamification-based quiz on the recall of formulaic sequences (FS). The experiment involved 87 freshmen enrolled in general English classes at a university in Seoul. As material, EFL textbooks based on content from popular franchises, such as the Marvel Cinematic Universe, Twilight, and Harry Potter, were used. The experiment was carried out as follows: first, vocabulary learning, second, reading comprehension, and third, writing. The fourth activity proceeded differently in two groups. The experimental group used gamification-based quiz to practice FS, whereas the comparison group summarized the reading. FS was evaluated using meaning recall and form recall. Consequently, no difference was found between the groups on meaning recall tests of FS, but the experimental group had a significantly higher average score than the comparison group on the post-test on the form recall of FS.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.233-258
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• 2022

The purpose of this study is to identify elementary school teachers' needs for access points when searching for picture books to be used as teaching media, and suggest ways to improve DLS(Digital Library System) in school libraries. To achieve this purpose, the study examined the access points provided by OPAC(Online Public Access Catalog) systems in seven domestic and foreign libraries. In addition, it conducted an online survey with elementary school teachers, and a total of 220 responses were finally analyzed. It was found that the most needed access points were topic, grade/age, content, subject/chapter, and cross-curricula learning topics, etc. Based on the results, this study suggests providing the most needed access points in DLS, developing controlled vocabulary tools, and improving system functions or the interface to enhance accessibility to picture books.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.393-402
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• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.