• Journal of Korean Elementary Science Education
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• v.33 no.4
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• pp.700-709
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• 2014

The purpose of this study was to examine the effects of question-generating strategy on science academic achievement, scientific attitude in elementary science class. To examine the effects of question-generating strategy this learning materials were applied to elementary science curriculum, and an experimental group and a control group were selected from $5^{th}$ graders at H elementary school located in Gyeonggi-do. Students were taught for 6 weeks. Control group take traditional lessons and solve questions presented textbook. Question generated group generate questions, solve them and feed back by themselves. The results of this study were found statistically significant difference in the pupil's enhancement of the science academic achievement, scientific attitude (p<.05). Thus question-generating strategy for elementary science inquiry instruction that has a positive effect on interests in class is useful and better be widely applied to science education.

• Journal of Korean Elementary Science Education
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• v.24 no.3
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• pp.249-258
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• 2005

The purpose of this study was to identify the type of thinking and generating processes of causal questions which were generated in preservice elementary teachers' observing activities. To find the generating processes of causal questions, 4 observing tasks, the task of grapes in soda, the candlelight, the celery, and the rock tasks, were administered to 7 preservice elementary teachers majoring in science education. The results of this study were as follows: The types of thinking in generating explicans exploration questions were classified as 8 types and explicans verification questions were classified as 9 types. The generating processes of explicans exploration questions were classified as 6 steps and explicans verification questions were classified as 5 steps. The results of this study may be used as a teaching strategy for guiding the direction and the method of scientific questions and developing the teaching-teaming programs that help student to generate scientifc questions.

• Journal of the Korean School Mathematics Society
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• v.24 no.2
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• pp.217-241
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• 2021

The purpose of this study is to qualitatively examine the effects of graphics representation of trigonometry modelling concerning question generating and idea sharing. The experimental setting(Experiment Group) was one class (N=26) at a public high school. The modelling process was designed as a process-oriented conceptualization divided into three steps i.e., (1) game with idea sharing and question generating, (2) graphic representation, and (3) symbolization in the mathematical applied tasks related to trigonometry function. The result indicates that Graphic Representation with Game Activity increases the opportunity of question generating and idea sharing during experimental work. Also, the results show that the introduction of computer graphics enhances the teaching of mathematical quantity in highschool classrooms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• 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 The Korean Association For Science Education
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• v.23 no.5
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• pp.458-469
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• 2003

Hypothesis is defined as a proposition intended as a possible explanation for an observed phenomenon. The purpose of this study was to generate a grounded theory on the process of undergraduate students' generating hypothesis-knowledge about scientific episodes. Three hypothesis-generating tasks were administered to four college students majored in science education. The present study showed that college students represented five types of intermediate knowledge in the process of hypothesis generation, such as question situation, hypothetical explicans, experienced situation, causal explicans, and final hypothetical knowledge. Furthermore, students used six types of thinking methods, such as searching knowledges, comparing a question situation and an experienced situation, borrowing explicans, combining explicans, selecting an explican, and confirming explicans. In addition, hypothesis-generating process involves inductive and deductive reasoning as well as abductive reasoning. This study also discusses the implications of these findings for teaching and evaluating in science education.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.27-36
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• 2019

In this paper, we propose a method to find the most similar answer to the user's response from the question-answer database in order to avoid generating a redundant question in retrieval-based automatic question generation system. As a question of the most similar answer to user's response may already be known to the user, the question should be removed from a set of question candidates. A similarity detector calculates a similarity between two answers by utilizing the same words, paraphrases, and sentential meanings. Paraphrases can be acquired by building a phrase table used in a statistical machine translation. A sentential meaning's similarity of two answers is calculated by an attention-based convolutional neural network. We evaluate the accuracy of the similarity detector on an evaluation set with 100 answers, and can get the 71% Mean Reciprocal Rank (MRR) score.

• Journal of The Korean Association For Science Education
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• v.30 no.6
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• pp.752-769
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• 2010

The purpose of this study was to apply the microgenetic analysis method for development of information on an individual's change in a certain area during a consistent time period to seek change in scientific questions that elementary school students create. The study subjects were six 6th graders in I elementary school located in Kyunggido with the students conducting 6 sessions of two observational tasks about dry grapes contained in soda pop and candlelight. Information were collected through students' scientific question development paper, record of field observation and interviews. The results of this study are as follows: first, the number of scientific questions that the elementary school students developed showed a tendency for reduction; second, the changes in type of scientific questions bring different results, which depend on a particular characteristic of the tasks; third, By observing pattern changes in scientific questions of each individual, it was found that different results show for each time for the same task, which in other words means that there exists variability within an individual. Also, variability between individuals were shown by confirming that the change pattern for each person were diverse. Thus, the result of this study shows the following implications on education of scientific question development. For students, scientific question development mean more opportunities to increase the process of developing and acquiring knowledge. Therefore, it is important to create situations where one can come up with scientific questions. In addition, analysis in tasks' nature when selecting tasks would be necessary to develop diverse scientific questions.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2
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• pp.173-179
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• 2004

Due to the insufficiency of training data in large vocabulary continuous speech recognition, similar context dependent phones can be clustered by decision trees to share the data. When the decision trees are built and used to predict unseen triphones, a phonetic question set is required. The phonetic question set, which contains categories of the phones with similar co-articulation effects, is usually generated by phonetic or linguistic experts. This knowledge-based approach for generating phonetic question set, however, may reduce the homogeneity of the clusters. Moreover, the experts must adjust the question sets whenever the language or the PLU (phone-like unit) of a recognition system is changed. Therefore, we propose a data-driven method to automatically generate phonetic question set. Since the proposed method generates the phone categories using speech data distribution, it is not dependent on the language or the PLU, and may enhance the homogeneity of the clusters. In large vocabulary speech recognition experiments, the proposed algorithm has been found to reduce the error rate by 14.3%.