• Communications of Mathematical Education
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• v.36 no.4
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• pp.487-509
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• 2022

The purpose of this study is to analyze classroom discourse in the math classroom of middle school in China, which has a unique math classroom background of entrance examination for high school. To this end, this study analyzed teacher question statistics and episodes by teacher question type as starting speech in mathematics classroom discourse, and five IRF subtypes were especially identified by class discourse structure analysis. The data were analyzed focusing on a total of 15 transcripts of math classes recorded by three math teachers at H School in Guiyang, Guizhou Province, China, and written interviews of teachers. According to the results of this study, an average of 20 teacher questions were observed for each class, and the teacher question type was classified into confirmation question (understanding confirmation question, explanation request question, and double check question) and information question (information presentation question). In addition, according to classroom discourse analysis, the IRF discourse structure was divided into fragmentary evaluation, evaluation+reason, evidence of explanation, evaluation+student response re-statement, guidance on other thoughts or solutions, and student answer correction or teacher opinion presentation.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.149-154
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• 2014

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.

• Journal of the Korean Society for information Management
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• v.21 no.2
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• pp.69-87
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• 2004

This study examines basic concepts and characteristics of QuestionPoint, the typical model of Collaborative Digital Reference Service and Global Reference Network, and analyzes the case of Question포인트 service at Korea Institute of Science and Technology Information where the service is initiated first time in Korea by developing Korean interface. 114 reference questions were analyzed, and questionnaires were sent to 63 service users to find out their overall attitudes and degree of satisfaction. Some suggestions were made by discussing the necessity of the enhancement of CDRS and mutual cooperation among Korean libraries.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• School Mathematics
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• v.13 no.2
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• pp.245-266
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• 2011

In this study, we surveyed on the 3rd and 4th grade teachers' perception and teaching practices towards open-ended question in the elementary mathematics textbook in the revised 7th curriculum. According to the result, teachers understood the purpose of open-ended question in general, but they recognized some problems in terms of open-ended question itself, difficulties found when they dealt with open-ended question in their classes, teacher's guide and teacher training. This research suggests ways of improvement of open-ended question on the basis of the survey results.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.144-147
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• 2015

Question Answering (QA) services can provide exact answers to user questions written in natural language form. This research focuses on how to build a QA system for a specific domain area. Online and offline QA system architecture of targeted domain such as domain detection, question analysis, reasoning, information retrieval, filtering, answer extraction, re-ranking, and answer generation, as well as data preparation are presented herein. Test results with an official Frequently Asked Question (FAQ) set showed 68% accuracy of the top 1 and 77% accuracy of the top 5. The contribution of each part such as question analysis system, document search engine, knowledge graph engine and re-ranking module for achieving the final answer are also presented.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.405-416
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• 2010

Knowledge search system is to find the question-answer documents for user question. Even highly qualified question-answer documents could be far different from those that a user want to find. The reason for this failure is that user frequently fails to make user's question to express his/her intension precisely. In this paper, we show our newly developed knowledge search system that recommends additional question-answer documents to include the contents that user want to find with high probability.

• Journal of Korean Elementary Science Education
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• v.31 no.1
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• pp.57-70
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• 2012

We investigated the perception and preferred type of question and analyzed type of questions asked by teachers in elementary science class to identify how teachers' perception of questioning is reflected in teaching practices. We collected the data from questionnaires, deep-interview and audiotaped four classes from grade 3 and six classes from grade 6. The data form deep-interview were analyzed interpretively and Blosser' framework of question was used to analyze questions which teachers used in classes. By interpretation of data from questionnaires, the teachers agreed that questioning affects science class in elementary school. There were a little differences in perceptions of questioning among three teachers. They preferred various types of question rather than a specific type. They didn't have a good understanding of questioning. The result showed that the teachers used frequently cognitive-memory question and convergent thinking question, which belonged to closed questions in their science classes. This didn't accord with their preferred types of question. The causes came from objectives of science instruction, degree of understanding about questioning, preference and confidence for science class. From this findings, we suggested that teachers should be given opportunities to take training courses in questioning in order to use effective questioning in science class.

• Journal of KIISE
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• v.43 no.10
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• pp.1085-1093
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• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• Journal of the korean veterinary medical association
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• v.44 no.7
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• pp.607-608
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• 2008