• Journal of Science Education
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• v.37 no.3
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• pp.513-524
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• 2013

The purpose of this study is to provide the proper methods of presenting question materials for generate of various question by comparing type, level, objectivity, manipulation of question in the presenting methods of question phenomena. I selected and showed actual objects, movies, and photographs as ways of presenting question materials, to each of which three question tasks were assigned. The generated questions by students were compared. The results showed that the question of conjectural, predictive, methodological, exploratory, verificational, qualitative, quantitative, simple-manipulative, pre-manipulative questions turned out to have significantly higher average frequencies in the cases of the presentation of photographs and movies than in the cases of the presentation of actual objects. However, the question of post-manipulative questions turned out to have significantly higher average frequencies in the cases of the presentation of actual objects than in the cases of the presentation of photographs and movies. and There were no significant differences between individual methods of question task presentation in average frequencies with respect to causal and methodological, subjective questions. Thus, we have learned from this that methods of presenting question phenomena had influence on the students' question. This suggests that we should consider forms of presentation of question materials in planning the teaching-learning of question.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.2
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• pp.101-123
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• 2021

This study analyses the questions and answers contained in the Knowledge Information Database of the collaborative digital reference service, 'Ask a librarian'. And based on the results of status of user requests, this study draws information usage behavior in the early stages of the service was derived. 1,124 Knowledge Information Database items out of 3,506 cases was analyzed by nine criterion. ① Number of questions and whether to be reference questions, ② Subject and keywords of the question, ③ Purpose of the question, ④ Type of question, ⑤ User's information request, ⑥ Information source and reference services provided by the librarian, ⑦ Number of days to answer, ⑧ Level of the participating library, ⑨ Question type by topic. As a results of analysis, first, users asked for reference questions from various topics as needed, rather than one from a similar topic at a time, but more than half of the total pure reference questions were from the field of library information science. Second, about 71.35% of users were using the 'Ask a librarian' service to recommend a list of information resources related to a particular topic or research problem, and there were also questions that required consultation on the reading situation. Third, the most preferred sources of information for users were bibliography, and in the case of online information sources, users did not relatively prefer them. Fourth, the number of days required to answer was able to confirm significant differences depending on the type of question and the level of the participating library. Fifth, 31.33% of the purpose of the general field question showed that were self-generated.

• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.277-286
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• 2004

This study investigated the types and frequencies of student-student questioning (SSQ) in a small group activities, 5 in one group or 2 in one group, during school experiments. Five girls of seventh grade were observed during school experiments and interviewed afterward. Between students, information-type questions were asked more frequently than thought-type questions. Most of the information-type questions were procedural ones and most of the thought-type questions were comprehension ones. However, thought-type questions did not make further discussion in the activities. The rate of answers in the case of 2 in one group was higher than that of 5 in one group. Moreover, the similar tendency was found when we investigated the rate of helpful question-answers. In a pair, lower achiever usually asked questions, not answered as much as in 5 in one group, and higher achiever answered. The frequency of SSQ in a pair was relatively low when there was a big difference of science achievements between pair members. In conclusion, information-type questions were asked more frequently than thought-type questions during school experiments and the rate of helpful question-answers was higher when group members was fewer.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.55-65
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• 2019

In programming language learning system of flip learning methods, the evaluation of cyber lectures generally proceeds from online to multiple choice questions. In this case, the questions are randomly extracted from the question bank and given to individual learners. In order for these evaluation results to be reflected in the grades, the equity of the examination question is more important than anything else. Especially in the programming language subject, the degree of difficulty that learners think can be different depending on the type of problem. In this paper, we classify the types of multiple-choice problems into two categories, and manage the difficulty level by each type. And we propose a question selection algorithm that considers both difficulty level and type of question. Considering the characteristics of the programming language, experimental results show that the proposed algorithm is more efficient and fair than the conventional method.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.375-383
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• 2010

In developing question-answering (QA) systems, it is hard to analyze natural language questions syntactically and semantically and to find exact answers to given query questions. In order to avoid these difficulties, we propose a new style of question-answering system that automatically generate natural language queries and can allow to search queries fit for given keywords. The key idea behind generating natural queries is that after significant sentences within text documents are applied to the named entity recognition technique, we can generate a natural query (interrogative sentence) for each named entity (such as person, location, and time). The natural query is divided into two types: simple type and sentence structure type. With the large database of question-answer pairs, the system can easily obtain natural queries and their corresponding answers for given keywords. The most important issue is how to generate meaningful queries which can present unambiguous answers. To this end, we propose two principles to decide which declarative sentences can be the sources of natural queries and a pattern-based method for generating meaningful queries from the selected sentences.

• Journal of Korean Elementary Science Education
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• v.30 no.4
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• pp.468-481
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• 2011

This research is to observe and analyze the student interactions when inquiry problems were generated along with the students by using asynchronous discussion methods with the class bulletin board. For this research, 10 students from a single class of 6th grade were selected. The subject students were divided into 2 groups by cognitive levels. After the students were submitted the 4 problem situations for 1 week each, the discussion process was analyzed. The research results are as follows. First, the analysis of the step by step interactive discussion showed that several students answered for the question from a single student while discussing first for the question and answer in a form of a question with many multiple answers without any connections with the previously asked questions. At the end of the discussion, one to two students answered to a question by taking turns and the type of discussion changed to one question - one answer type by answering to the person who spoke prior to the next. Second, the discussion took place with the students in the transitional stage speaking in time in order, to provide comments to the bottom of the linear form and students in the formal operational stage students speaking in temporal order, regardless of the number of comments in the direction of the radiation(mind map) forms. The individual comment speaking rates were similar in the two groups so the students were able to speak indiscriminately.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.529-536
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• 2004

The main idea of this paper is to combine position information in sentence and query type classification to make the documents ranking to query more accessible. First, the use of conceptual graphs for the representation of document contents In information retrieval is discussed. The method is based on well-known strategies of text comparison, such as Dice Coefficient, with position-based weighted term. Second, we introduce a method for learning query type classification that improves the ability to retrieve answers to questions from Question Answering system. Proposed methods employ naive bayes classification in machine learning fields. And, we used a collection of approximately 30,000 question-answer pairs for training, obtained from Frequently Asked Question(FAQ) files on various subjects. The evaluation on a set of queries from international TREC-9 question answering track shows that the method with machine learning outperforms the underline other systems in TREC-9 (0.29 for mean reciprocal rank and 55.1% for precision).

• KIPS Transactions on Software and Data Engineering
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• v.4 no.7
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• pp.283-290
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• 2015

In this paper, we propose a hybrid method for the recognition of answer types in the WiseQA system. The answer types are classified into two categories: the lexical answer type (LAT) and the semantic answer type (SAT). This paper proposes two models for the LAT detection. One is a rule-based model using question focuses. The other is a machine learning model based on sequence labeling. We also propose two models for the SAT classification. They are a machine learning model based on multiclass classification and a filtering-rule model based on the lexical answer type. The performance of the LAT detection and the SAT classification shows F1-score of 82.47% and precision of 77.13%, respectively. Compared with IBM Watson for the performance of the LAT, the precision is 1.0% lower and the recall is 7.4% higher.

• Journal of Korean language education
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• v.29 no.3
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• pp.1-24
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• 2018

The purpose of this study was to analyze teachers' questions in the actual general Korean classroom for academic purposes and identify types of questions. The results of the question analysis by type identified 713 teacher's questions in total: echoic questions made up 41% while epistemic questions were 19.3% and expended question turned out to make up 39.7%. 'Comprehension check questions' were 29%, which was a major part in the echoic question. 'Referential questions' were a major part in the epistemic question. Also, the research discovered that 'knowledge integration' questions held the largest majority in expended questions. Since the teacher-led lecture was often conducted in the problem-presentation stage, the percentage of Echoic question was high; and moreover, the problem-solving stage promoted to come up with more improved solutions of the problem. In the outcome and presentation stage, it was discovered that the questions aimed to check understanding of content in the subject and expand thoughts. Therefore, it is necessary to develop strategies for teacher's questions by phase and further conduct research on the interaction between learners and teacher's questions in the future.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.319-330
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• 2021

Recently, in the field of open domain natural language question answering, multi-task, multi-hop question answering has been studied extensively. In this paper, we propose a novel deep neural network model using hierarchical graphs to answer effectively such multi-task, multi-hop questions. The proposed model extracts different levels of contextual information from multiple paragraphs using hierarchical graphs and graph neural networks, and then utilize them to predict answer type, supporting sentences and answer spans simultaneously. Conducting experiments with the HotpotQA benchmark dataset, we show high performance and positive effects of the proposed model.