• The Mathematical Education
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• v.49 no.3
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• pp.299-311
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• 2010

The purpose of this study was to examine the effects of abstraction offer of basic concept principle and feedback of self-efficacy attributional and mathematical study ability of math underachievers in high school based on the attribution theory and self-efficacy theory. The hypothesis were posed as below : Hypothesis 1: The experimental group that takes the abstraction offer of concept principle and attributional feedback training would be better at most self-efficacy than the control group that doesn't. Hypothesis 2: The experimental group that takes the abstraction offer of concept principle and attributional feedback training would have better math achievement than the control group that doesn't. They were divided into an experimental group and a control group, and the attribution disposition, self-efficacy and academic achievement of the children were measured by pretest and posttest. For data analysis, SPSS/PC+ program was employed and t-test was conducted. The main findings of this study were as below : First, the abstraction offer of concept principle and attributional feedback training was effective for enhancing the math self-efficacy in high school underachievers. Second, the abstraction offer of concept principle and attributional feedback training was effective for increasing the math achievement in high school underachievers.

• The Mathematical Education
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• v.42 no.3
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• pp.303-325
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• 2003

This study sought to provide an explanation of university students' concept understanding on the infinity and infinite process and utilized a psychological constructivist perspective to examine the differences in transitions that students make from static concept of limit to actualized infinity stage in context of problems. Open-ended questions were used to gather data that were used to develop an explanation concerning student understanding. 47 university students answered individually and were asked to solve 16 tasks developed by Petty(1996). Microgenetic method with two cases from the expert-novice perspective were used to develop and substantiate an explanation regarding students' transitions from static concept of limit to actualized infinity stage. The protocols were analyzed to document student conceptions. Cifarelli(1988)'s levels of reflective abstraction and Robert(1982) and Sierpinska(1985)'s three-stage concept development model of infinity and infinite process provided a framework for this explanation. Students who completed a transition to actualized infinity operated higher levels of reflective abstraction than students who was unable to complete such a transition. Developing this ability was found to be critical in achieving about understanding the concept of infinity and infinite process.

• School Mathematics
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• v.15 no.4
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• pp.785-799
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• 2013

The researchers developed the teaching-learning materials for 9th grade mathematically gifted students in terms of the hypothesis that the students would have opportunity for problem solving and develop various mathematical thinking through the mathematical modeling lessons. The researchers analyzed what mathematical thinking abilities were shown on each stage of modeling process through the application of the materials. Organization of information ability appears in the real-world exploratory stage. Intuition insight ability, spatialization/visualization ability, mathematical reasoning ability and reflective thinking ability appears in the pre-mathematical model development stage. Mathematical abstraction ability, spatialization/visualization ability, mathematical reasoning ability and reflective thinking ability appears in the mathematical model development stage. Generalization and application ability and reflective thinking ability appears in the model application stage. The developed modeling assignments have provided the opportunities for mathematically-gifted students' mathematical thinking ability to develop and expand.

• Journal of Gifted/Talented Education
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• v.8 no.2
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• pp.69-89
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• 1998

The purpose of this study is to develop a test which can be used in identification of the gifted students in the area of mathematics. This study was carried out for two years from 1996. Mathematical giftedness is, in this study, regarded as a result of interaction of mathematical thinking ability, mathematical creativity, mathematical task committment, background knowledge. This study presumed that mathematical thinking ability is composed of seven thinking abilities: intuitive insights, ability for information organization, ability for visualization, ability for mathematical abstraction, inferential thinking ability(both inductive and deductive thinking abilities), generalization and application ability, and reflective thinking. This study also presupposed that mathematical creativity is composed of 3 characteristics: fluency, flexibility, originality. The test for mathematical creative problem solving ability was developed for primary, middle, and high school students. The test is composed of two parts: the first part is concentrated more on divergent thinking, while the second part is more on convergent thinking. The major targets of the test were the students whose achievement level in mathematics belong to top 15~20% in each school. The goodness of the test was examined in the aspects of reliability, validity, difficulty, and discrimination power. Cronbach $\alpha$ was in the range of .60~.75, suggesting that the test is fairly reliable. The validity of the test was examined through the correlation among the test results for mathematical creative problem solving ability, I. Q., and academic achievement scores in mathematics and through the correlation between the scores in the first part and the scores in the second part of the test for mathematical creative problem solving ability. The test was found to be very difficult for the subjects. However, the discrimination power of the test was at the acceptable level.

• Communications of Mathematical Education
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• v.25 no.1
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• pp.245-259
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• 2011

In this study, the instrument of mathematical creative problem solving ability test were considered the differences between Korean and American sixth grade students in mathematical creativity ability and mathematical thinking ability. The instrument consists of 9 items. The participants for the study were 212 Korean and 148 American students. SPSS were carried out to verify the validities and reliability. Reliabilities(Cronbach ${\alpha}$) in mathematical creativity ability is 0.9047 and in mathematical thinking ability is 0.9299 which were satisfied internal validity evaluation on the test items. Internal validity were analyzed by BIGSTEPS based on Rasch's 1-parameter item response model. The results of this study can serve as a foundation for understanding the Korean and American students differences in mathematical creativity ability and mathematical thinking ability. Especially we get the some informations on mathematical creativity ability for American's fifth grade to seventh grade students.

• Communications of Mathematical Education
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• v.20 no.1 s.25
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• pp.117-145
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• 2006

Mathematically gifted children have creative curiosity about novel tasks deriving from their natural mathematical talents, aptitudes, intellectual abilities and creativities. More effect in nurturing the creative thinking found in brilliant children, letting them approach problem solving in various ways and make strategic attempts is needed. Given this perspective, it is desirable to select open-ended and atypical problems as a task for educational program for gifted children. In this paper, various types of open-ended problems were framed and based on these, teaming activities were adapted into gifted children's class. Then in the problem solving process, the characteristic of bright children's mathematical thinking ability and examples of problem solving strategies were analyzed so that suggestions about classes for bright children utilizing open-ended tasks at elementary schools could be achieved. For this, an open-ended task made of 24 inquiries was structured, the teaching procedure was made of three steps properly transforming Renzulli's Enrichment Triad Model, and 24 periods of classes were progressed according to the teaching plan. One period of class for each subcategories of mathematical thinking ability; ability of intuitional insight, systematizing information, space formation/visualization, mathematical abstraction, mathematical reasoning, and reflective thinking were chosen and analyzed regarding teaching, teaming process and products. Problem solving examples that could be anticipated through teaching and teaming process and products analysis, and creative problem solving examples were suggested, and suggestions about teaching bright children using open-ended tasks were deduced based on the analysis of the characteristic of tasks, role of the teacher, impartiality and probability of approaching through reflecting the classes. Through the case study of a mathematics class for bright children making use of open-ended tasks proved to satisfy the curiosity of the students, and was proved to be effective for providing and forming a habit of various mathematical thinking experiences by establishing atypical mathematical problem solving strategies. This study is meaningful in that it provided mathematically gifted children's problem solving procedures about open-ended problems and it made an attempt at concrete and practical case study about classes fur gifted children while most of studies on education for gifted children in this country focus on the studies on basic theories or quantitative studies.

• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.199-216
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• 1998

One of the main features of the 7th revised national curriculum is the implementation of a 'Differentiated Curriculum'. Differentiated Curriculum is often interpreted as meaning the same as 'tracking' or 'ability grouping' in western countries. In the 7th revised curriculum, mathematics is organized and implemented by 'Level-Based Differentiated Curriculum'. To develop mathematics textbooks and teaching-and-learning materials for Differentiated Curriculum, the ideas of 'Enriched and Supplemental Differentiated Curriculum'need to be applied, that is, to provide advanced contents for fast learners, and plain contents for slow learners. Level Based Differentiated Curriculum could be implemented by ability grouping either between classes or within classes. According to these two exemplary models, the implementation models for supplemental course and enriched course are determined. The contents for supplemental course are comprised of minimal essential elements selected from the standard course at a decreased level of complexity and abstraction. The contents of enriched courses are focused on various applications of mathematical knowledge in the real world. Special remedy course will be offered to extremely underachieved students, The principles of developing teaching-and-learning material for special remedy course were obtained from the histo-genetic principle, progressive mathematizing principle, and constructivism.

• Journal of the Korean School Mathematics Society
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• v.12 no.1
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• pp.47-70
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• 2009

The purpose of this research was to confirm one of constructivists' assumptions that even children 조o are with low reasoning ability can make reflective abstracting ability and cognitive structures by this ability can make generation ability of new knowledge by themselves. To investigate the assumption, learner-centered instruction were implemented to 2nd grade classroom located in Suseong Gu, DaeGu City and with lesson plans which initially were developed by Burns and corrected by the researchers. Recordings videoed using 2 video cameras, observations, instructions, children's activity worksheets, instruction journals were analyzed using multiple tests for qualitative analysis. Some conclusions are drawn from the results. First, even children with low reasoning ability can construct mathematical knowledge on multiplication in their own. ways, Thus, teachers should not compel them to learn a learning lesson's goals which is demanded in traditional instruction, with having belief they have reasoning ability. Second, teachers need to have the perspectives of respects out of each child in their classroom and provide some materials which can provoke children's cognitive conflict and promote thinking with the recognition of effectiveness of learner-centered instruction. Third, students try to develop their ability of reflective and therefore establish cognitive structures such as webs, not isolated and fragmental ones.

• Journal of Educational Research in Mathematics
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• v.17 no.1
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• pp.33-50
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• 2007

Positive attitude toward mathematics is gaining bigger recognition as an important contributing factor to mathematical ability. As a strategy for strengthening affective domain and betterment of mathematics teaching and loaming, classifying students by their causes for liking or disliking mathematics can be an effective way In this study the author tried to devise methods to classify students by their types of math disliking and investigate correlations between mathematical achievements and these math-disliking types from a sample group of 8th graders. To identify the types of reasons why 8th graders dislike mathematics, a questionnaire with 30 items was made firstly. Then by applying the 'Factor analysis' of SPSS, the 30 items were divided into five partitions. Through abstraction of each partition, five math-disliking types, 'Competences', 'Basics', 'Confidences', 'Usefulness', and 'Teachers' were defined. They are expected to help teachers for describing each student's tendency of math-disliking. Further, correlation coefficients between mathematical achievements and each of the five math-disliking type were investigated against 4 groups which were made from sample group by the discrimination of gender and two levels (high and low) of mathematical achievements in cognitive area. As results, the following facts were found. (i) The trends of correlations between cognitive achievement and the five math disliking types were different across the 4 groups at statistically meaningful degrees. (ii) Most of the male students who had math-disliking types were proved to be in the low achievement level. But for the female students, only 50% of students who had math-disliking types were in the low achievement level. (iii) Compared to male students, higher portion of female students had math-disliking types despite their high achievement in cognitive area.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.