• English Language & Literature Teaching
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• v.17 no.1
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• pp.17-35
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• 2011

The purpose of this study was to explore the effect of an explicit word instruction for EFL beginning readers and their perception on the learning experience. Data were attained from 16 fourth graders who took English class as a development activity. Data include the results of pre- and post-test of high frequency sight word recognition, oral reading ability, students' survey responses, and teacher observation. The descriptive statistics were obtained for the result of the pre- and post-test. The findings from the student survey and teacher observation were also provided and interpreted to better understand the result of project and students' perception on the learning experience. The followings are the results of this study. The word recognition ability of the students was dramatically improved after the project. The students were satisfied with the overall learning experience perceiving it as helpful and fun learning. They expressed that the explicit word instruction helped their word recognition and reading ability. The results also supported that the confidence of students on their reading ability were heightened. Several suggestions are made for teachers and researchers on the word instruction for young EFL learners who are beginning readers.

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.107-134
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• 2012

The purpose of this study is to investigate the effectiveness of two teaching methods of word problems, one based on mathematical modeling learning(ML) and the other on traditional learning(TL). Additionally, the influence of mathematical modeling learning in word problem solving behavior, application ability of real world experiences in word problem solving and the beliefs of word problem solving will be examined. The results of this study were as follows: First, as to word problem solving behavior, there was a significant difference between the two groups. This mean that the ML was effective for word problem solving behavior. Second, all of the students in the ML group and the TL group had a strong tendency to exclude real world knowledge and sense-making when solving word problems during the pre-test. but A significant difference appeared between the two groups during post-test. classroom culture improvement efforts. Third, mathematical modeling learning(ML) was effective for improvement of traditional beliefs about word problems. Fourth, mathematical modeling learning(ML) exerted more influence on mathematically strong and average students and a positive effect to mathematically weak students. High and average-level students tended to benefit from mathematical modeling learning(ML) more than their low-level peers. This difference was caused by less involvement from low-level students in group assignments and whole-class discussions. While using the mathematical modeling learning method, elementary students were able to build various models about problem situations, justify, and elaborate models by discussions and comparisons from each other. This proves that elementary students could participate in mathematical modeling activities via word problems, it results form the use of more authentic tasks, small group activities and whole-class discussions, exclusion of teacher's direct intervention, and classroom culture improvement efforts. The conclusions drawn from the results obtained in this study are as follows: First, mathematical modeling learning(ML) can become an effective method, guiding word problem solving behavior from the direct translation approach(DTA) based on numbers and key words without understanding about problem situations to the meaningful based approach(MBA) building rich models for problem situations. Second, mathematical modeling learning(ML) will contribute attitudes considering real world situations in solving word problems. Mathematical modeling activities for word problems can help elementary students to understand relations between word problems and the real world. It will be also help them to develop the ability to look at the real world mathematically. Third, mathematical modeling learning(ML) will contribute to the development of positive beliefs for mathematics and word problem solving. Word problem teaching focused on just mathematical operations can't develop proper beliefs for mathematics and word problem solving. Mathematical modeling learning(ML) for word problems provide elementary students the opportunity to understand the real world mathematically, and it increases students' modeling abilities. Futhermore, it is a very useful method of reforming the current problems of word problem teaching and learning. Therefore, word problems in school mathematics should be replaced by more authentic ones and modeling activities should be introduced early in elementary school eduction, which would help change the perceptions about word problem teaching.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.171-176
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• 2022

With the recent development of deep learning technology, the areas of recommendation systems have also diversified. This paper studied algorithms to improve the learning rate and studied the significance results according to words through comparison with the performance characteristics of the Word2Vec model. The problem recommendation algorithm was implemented with the values expressed through the reflection of meaning and similarity test between texts, which are characteristics of the Word2Vec model. Through Word2Vec's learning results, problem recommendations were conducted using text similarity values, and problems with high similarity can be recommended. In the experimental process, it was seen that the accuracy decreased with the quantitative amount of data, and it was confirmed that the larger the amount of data in the data set, the higher the accuracy.

• Journal of Korea Game Society
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• v.10 no.4
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• pp.35-48
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• 2010

The purpose of the present study was to suggest basic word lists and contents of English for mobile games and to propose how to apply basic word lists to mobile games for English vocabulary learning because it is possible that some potential advantages of mobile games can provide elementary school students with new learning environment for English vocabulary learning. To support this idea, firstly, the basic word lists were selected from essential word lists in the national English curriculum, the word lists from textbooks, and five randomly chosen word books. Secondly, mobile games were examined and appropriate mobile games for learning English words was selected. Lastly, the basic word lists for mobile games were applied to the selected game.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.271-286
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• 2006

In this paper, we introduce automatic question generation technique using the language resources like User-Word Intelligent Network(U-WIN) and Korean dictionary including quite a for of information. And we present Korean word learning system with this technique. The item pool method which almost learning-system are using makes some problems. As a solution of the problems, we classified into 8 question type and implemented the Korean word learning system which is making the Korean question automatically by using the morphological and semantic information according to the automatic question generation pattern of each type.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.31-41
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• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• Journal of Korea Game Society
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• v.12 no.6
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• pp.95-106
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• 2012

Emotion is very important feature in educational situation. Because it has high influence to memory, educational achievement, motivation. This study tried to find out possibility of serious game as emotional engagement tool in educational situation. We did our pilot experiment to elementary school students who are english as second language. In this L2 learning situation, we did our basic experiment with English language learning serious game called 'Word Collectrian". Word Collectrian has some features for emotional engagement. It has interaction for dynamic word visualization, providing context video for word usage, putting visualized word on learner's virtual home. According to experimental result, word Collectrian has possibility for educational achievement and emotional engagement effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1300-1306
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• 2018

The purpose of this study is to see possibility of Char2Vec as alternative of Word2Vec that most famous word embedding model in Sentence Similarity Measure Problem by Deep-Learning. In experiment, we used the Siamese Ma-LSTM recurrent neural network architecture for measure similarity two random sentences. Siamese Ma-LSTM model was implemented with tensorflow. We train each model with 200 epoch on gpu environment and it took about 20 hours. Then we compared Word2Vec based model training result with Char2Vec based model training result. as a result, model of based with Char2Vec that initialized random weight record 75.1% validation dataset accuracy and model of based with Word2Vec that pretrained with 3 million words and phrase record 71.6% validation dataset accuracy. so Char2Vec is suitable alternate of Word2Vec to optimize high system memory requirements problem.