• Journal of Educational Research in Mathematics
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• v.19 no.1
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• pp.81-98
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• 2009

This study analyzed the types of quantitative reasoning and the characteristics of representation in order to figure out the characteristics of quantitative reasoning of the sixth graders. Three students who used quantitative reasoning in solving problems were interviewed in depth. Results showed that the three students used two types of quantitative reasoning, that is difference reasoning and multiplicative reasoning. They used qualitatively different quantitative reasoning, which had a great impact on their problem-solving strategy. Students used symbolic, linguistic and visual representations. Particularly, they used visual representations to represent quantities and relations between quantities included in the problem situation, and to deduce a new relation between quantities. This result implies that visual representation plays a prominent role in quantitative reasoning. This paper included several implications on quantitative reasoning and quantitative approach related to early algebra education.

• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• The Mathematical Education
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• v.62 no.4
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• pp.457-471
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• 2023

Measurement is an imperative content area of early elementary mathematics, but it is reported that students' understanding of units in measurement situations is insufficient despite its importance. Therefore, this study examined lower-grade elementary students' quantitative reasoning of units in length measurement by identifying the levels of reasoning of units. For this purpose, we collected and analyzed the responses of second-grade elementary school students who engaged in a set of length measurement tasks using an open number line in terms of unitizing, iterating, and partitioning. As a result of the study, we categorized students' quantitative reasoning of unit levels into four levels: Iterating unit one, Iterating a given unit, Relating units, and Transforming units. The most prevalent level was Relating units, which is the level of recognizing relationships between units to measure length. Each level was illustrated with distinct features and examples of unit reasoning. Based on the results of this study, a personalized plan to the level of unit reasoning of students is required, and the need for additional guidance or the use of customized interventions for students with incomplete unit reasoning skills is necessary.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12B
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• pp.767-778
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• 2007

In this paper, we propose a Intelligent Service Reasoning (ISR) model using data mining in smart home environments. Our model creates a service tree used for service reasoning on the basis of C4.5 algorithm, one of decision tree algorithms, and reasons service that will be offered to users through quantitative weight estimation algorithm that uses quantitative characteristic rule and quantitative discriminant rule. The effectiveness in the performance of the developed model is validated through a smart home-network simulation.

• School Mathematics
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• v.14 no.4
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• pp.445-468
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• 2012

This study is tried in order to link informal arithmetic reasoning to formal algebraic reasoning. In this study, we investigated elementary school student's non-formal algebraic reasoning used in algebraic problem solving. The result of we investigated algebraic reasoning of 839 students from grade 1 to 6 in two schools, Korea, we could recognize that they used various arithmetic reasoning and pre-formal algebraic reasoning which is the other than that is proposed in the text book in word problem solving related to the linear systems of equation. Reasoning strategies were diverse depending on structure of meaning and operational of problems. And we analyzed the cause of failure of reasoning in algebraic problem solving. Especially, 'quantitative reasoning', 'proportional reasoning' are turned into 'non-formal method of substitution' and 'non-formal method of addition and subtraction'. We discussed possibilities that we are able to connect these pre-formal algebraic reasoning to formal algebraic reasoning.

• Journal of Elementary Mathematics Education in Korea
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• v.19 no.4
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• pp.457-484
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• 2015

This study aims to investigate an approach to teach proportional reasoning in elementary mathematics class by analyzing the proportional strategies the students use to solve the proportional reasoning tasks and their percentages of correct answers. For this research 174 sixth graders are examined. The instrument test consists of various questions types in reference to the previous study; the proportional reasoning tasks are divided into algebraic-geometric, quantitative-qualitative and missing value-comparisons tasks. Comparing the percentages of correct answers according to the task types, the algebraic tasks are higher than the geometric tasks, quantitative tasks are higher than the qualitative tasks, and missing value tasks are higher than the comparisons tasks. As to the strategies that students employed, the percentage of using the informal strategy such as factor strategy and unit rate strategy is relatively higher than that of using the formal strategy, even after learning the cross product strategy. As an insightful approach for teaching proportional reasoning, based on the study results, it is suggested to teach the informal strategy explicitly instead of the informal strategy, reinforce the qualitative reasoning while combining the qualitative with the quantitative reasoning, and balance the various task types in the mathematics classroom.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.228-231
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• 1998

Artificial Intelligence literatures have recognized that stock market is a highly unstructured and complex domain so that it is difficult to find knowledge that belongs to that domain. This paper demonstrates that the proposed QCOM can derive global knowledge about stock market on the basis of a set of local knowledge and express it as a digraph representation. In addition, inference mechanism using quantitative causal reasoning can describe the qualitative and quantitative effects of exogenous variables on stock market.

• The Mathematical Education
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• v.55 no.3
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• pp.251-279
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• 2016

There are many studies on 'how' students solve mathematical problems, but few of them sufficiently explained 'why' they have to solve the problems in their own different ways. As quantitative reasoning is the basis for algebraic reasoning, to scrutinize a student's way of dealing with quantities in a problem situation is critical for understanding why the student has to solve it in such a way. From our teaching experiments with two ninth-grade students, we found that emergences of a certain level of covariational reasoning were highly consistent across different types of problems within each participating student. They conceived the given problem situations at different levels of covariation and constructed their own quantity-structures. It led them to solve the problems with the resources accessible to their structures only, and never reconciled with the other's solving strategies even after having reflection and discussion on their solutions. It indicates that their own structure of quantities constrained the whole process of problem solving and they could not discard the structures. Based on the results, we argue that teachers, in order to provide practical supports for students' problem solving, need to focus on the students' way of covariational reasoning of problem situations.

• Journal of Educational Research in Mathematics
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• v.27 no.1
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• pp.23-49
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• 2017

This study examined two current learning models for covariational reasoning(Carlson et al.(2002), Thompson, & Carlson(2017)), applied the models to teaching two $9^{th}$ grade students, and analyzed the results according to the types of graphs(a quantitative graph or qualitative graph). Results showed that the model of Thompson and Carlson(2017) was more useful than that of Carlson et al.(2002) in figuring out the students' levels in their quantitative graphing activities. Applying Carlson et al.(2002)'s model made it possible to classify levels of the students in their qualitative graphs. The results of this study suggest that not only quantitative understanding but also qualitative understanding is important in investigating students' covariational reasoning levels. The model of Thompson and Carlson(2017) reveals more various aspects in exploring students' levels of quantitative understanding, and the model of Carlson et al.(2002) revealing more of qualitative understanding.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.531-535
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• 1996

Computer aided conceptual solution of engineering problems can be effectively implemented by qualitative reasoning based on a physical model. Qualitative reasoning needs modeling paradigm which provides intellignet control of modeling assumptions and robust inferences without quantitative information about the system. We developed reasoning method using new algebra of qualitative mathematics. The method is applied to a conceptual design scheme of anadaptive control system of cutting process. The method identifies differences between proportional and proportional-integral control scheme of cutting process. It is shown that unfeasible investment could be prevented in the early conceptual stage by the qualitative reasoning procedures proposed in this paper.