• School Mathematics
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• v.9 no.1
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• pp.45-64
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• 2007

The primary purposes of this study were to investigate how sixth graders would react to the types of tasks with regard to the comprehension of graphs and what differences might be among the kinds of graphs, and to raise issues about instructional methods of graphs. A descriptive study through pencil-and-paper tests was conducted. The tests consisted of 48 questions with 4 types of tasks (reading the data, reading between the data, reading beyond the data, and understanding the situations) and 6 kinds of graphs. The conclusions drawn from the results obtained in this study were as follows: First, it is necessary to foster the ability of interpreting the data and understanding the situation in graphs as well as that of reading the data and finding out the relationships in the data. Second, it is informative for teachers to know students' difficulties and thinking processes. Third, in order to develop understanding of graphs, it is important that students solve different types of tasks beyond simple question-answer tasks. Fourth, teachers need to pay attention to teach fundamental factors such as reading the data with regard to line graphs and stem-and-leaf plots Finally, graph type and task type interact to determine graph-comprehension performance. Therefore, both learning all kinds of graphs and being familiar with multiple types of tasks are important.

• Journal of The Korean Association For Science Education
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• v.25 no.6
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• pp.671-677
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• 2005

Line graphs are powerful tools in conveying complicated relationships and ideas because line graphs show the relationship that exists between two continuous variables. Also, line graphs can show readers the variations in variables and correlate two variables in a two dimensional space. For these reasons, line graphs have a significant role in physics, especially kinematics. To what extent are Korean college and secondary students able to understand kinematics graphs? Is there a difference between American students and Korean students in interpreting kinematics graphs? The TUG-K instrument (Test of Understanding Graphs in Kinematics) was administered to students in both countries. The results show the difference between American students and Korean students by TUG-K objective. Also, the results are discussed in terms of a graph comprehension theory.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.419-430
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• 2009

Reading comprehension (RC) systems are to understand a given text and return answers in response to questions about the text. Many previous studies extract sentences that are the most similar to questions as answers. However, texts for RC tests are generally short and facts about an event or entity are often expressed in multiple sentences. The answers for some questions might be indirectly presented in the sentences having few overlapping words with the questions. This paper proposes a conceptual graph matching method towards RC tests to extract answer strings. The method first represents the text and questions as conceptual graphs, and then extracts subgraphs for every candidate answer concept from the text graph. All candidate answer concepts will be scored and ranked according to the matching similarity between their sub-graphs and question graph. The top one will be returned as answer seed to form a concise answer string. Since the sub-graphs for candidate answer concepts are not restricted to only covering a single sentence, our approach improved the performance of answer extraction on the Remedia test data.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.155-161
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• 2006

To enhance the interest and comprehension of learners studying Statistics. The program which learners can use is needed. With the help of this program, the interest and concentration of the learners can be enhanced, and the effects of the study of Statistics can be maximized, through the convenience of calculation to the theoretical contents of Statistics, various graphs, the, simulation.

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.2
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• pp.247-272
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• 2019

The purpose of this study is to analyze the teaching of statistical graphs according to elementary mathematics curriculum. To do this, we set up three research questions as follows. First, what is the change in achievement standards related to the teaching of statistical graphs in elementary mathematics curriculum? Second, what is the change in the teaching of the drawing of statistical graphs in elementary mathematics curriculum? Third, what is the change in the teaching of understanding of statistical graphs in elementary mathematics curriculum? For the first research question, we analyzed the achievement standards related to the teaching of the statistics of the 2015 revised curriculum from the first curriculum. For the second research question, we analyzed how to provide students with the opportunity to draw graphs(number of drawings) and whether to present the basic frame of the graphs(frame provided). For the third research question, we analyzed questions in the textbooks based on the graph understanding; 'reading data', 'finding the relation between data', 'interpreting data', 'understanding the situation'. As a result of the analysis, the achievement standard in the curriculum has changed in the direction of fostering statistical thinking, and it has been changed to emphasize the interpretation of the graph so as to make the graph drawing easy. However, 'interpreting data' and 'understanding the situation' were still lacking.

• Journal of the Korean School Mathematics Society
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• v.11 no.4
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• pp.655-676
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• 2008

This paper analyzed contents related to statistics in the 7th elementary mathematics curriculum in conjunction with main changes in the recent revised curriculum. This paper then provided detailed analyses of textbooks and workbooks in terms of main contents, instructional methods of graphs, and comprehension of graphs required to students in order to look closely at how such instructional materials would put the vision of the curriculum into action. It is expected that the issues and suggestions stemming from this analysis are informative in constructing new textbooks and workbooks.

• Knowledge Management Research
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• v.25 no.2
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• pp.1-18
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• 2024

With the increasing demand for and utilization of large language models (LLMs), the risk of user sensitive information being inputted and leaked during the use of LLMs also escalates. Typically recognized as a tool for mitigating the hallucination issues of LLMs, knowledge graphs, constructed independently from LLMs, can store and manage sensitive user information separately, thereby minimizing the potential for data breaches. This study, therefore, presents a knowledge graph-based chatbot that transforms user-inputted natural language questions into queries appropriate for the knowledge graph using LLMs, subsequently executing these queries and extracting the results. Furthermore, to evaluate the functional validity of the developed knowledge graph-based chatbot, performance tests are conducted to assess the comprehension and adaptability to existing knowledge graphs, the capability to create new entity classes, and the accessibility of LLMs to the knowledge graph content.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.