• School Mathematics
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• v.5 no.3
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• pp.361-384
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• 2003

The purpose of this study was to investigate students' problem solving process based on the model of IDEAL if they learn to solve word problems of simultaneous linear equations through structure-representation instruction. The problem solving model of IDEAL is followed by stages; identifying problems(I), defining problems(D), exploring alternative approaches(E), acting on a plan(A). 160 second-grade students of middle schools participated in a study was classified into those of (a) a control group receiving no explicit instruction of structure-representation in word problem solving, and (b) a group receiving structure-representation instruction followed by IDEAL. As a result of this study, a structure-representation instruction improved word-problem solving performance and the students taught by the structure-representation approach discriminate more sharply equivalent problem, isomorphic problem and similar problem than the students of a control group. Also, students of the group instructed by structure-representation approach have less errors in understanding contexts and using data, in transferring mathematical symbol from internal learning relation of word problem and in setting up an equation than the students of a control group. Especially, this study shows that the model of direct transformation and the model of structure-schema in students' problem solving process of I and D stages.

• School Mathematics
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• v.18 no.2
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• pp.323-348
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• 2016

The 2009 revised national mathematics curriculum have inserted mathematical 'linking cube' activities in the 6th grade math classes to improve students' spatial problem solving abilities and communication skills. However, we found that it was hard for teachers to teach problem solving and communication skills due to the absence of mathematical way of representing linking cubes in the classroom. In this paper, we propose 3D 'turtle representation system' as teaching and learning tools for linking cube activities. After using turtle representation system for linking cube activities, teachers responded that turtle representation system is a valuable problem solving and communication tools for the linking cube mathematics classes. We conclude that turtle representation system is a well designed teaching and learning tools for linking cube activities, and there are lots of educational meanings in the 3D turtle representation system.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.1-8
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• 2018

It is not easy to learn simple expressions of moving picture data since it contains noise and a lot of information in addition to time-based information. In this study, we propose a similarity representation method and a deep learning method between sequential data which can express such video data abstractly and simpler. This is to learn and obtain a function that allow them to have maximum information when interpreting the degree of similarity between image data vectors constituting a moving picture. Through the actual data, it is confirmed that the proposed method shows better classification performance than the existing moving image classification methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2093-2108
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• 2017

In this paper, we describe a novel method for recognizing human actions from different views via view knowledge transfer. Our approach is characterized by two aspects: 1) We propose a unsupervised topic transfer model (TTM) to model two view-dependent vocabularies, where the original bag of visual words (BoVW) representation can be transferred into a bag of topics (BoT) representation. The higher-level BoT features, which can be shared across views, can connect action models for different views. 2) Our features make it possible to obtain a discriminative model of action under one view and categorize actions in another view. We tested our approach on the IXMAS data set, and the results are promising, given such a simple approach. In addition, we also demonstrate a supervised topic transfer model (STTM), which can combine transfer feature learning and discriminative classifier learning into one framework.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.26-39
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• 2020

Visual Representation Competence Taxonomy (VRC-T) was developed in previous study(Yoon, 2018) to provide a framework conducive to assess visual representation competence and to devise appropriate educational activities for it. This study is an extension of the previous study. It aimed to explore the usefulness of VRC-T and revise it by analyzing the patterns of visual representation use in science lessons. The researcher collected lesson plans on shadow principle from 11 pre-service and 13 in-service elementary teachers and conducted individual interviews regarding what visual representations they considered and how they tried to use them in science lessons. VRC-T was used as an analytical framework to examine the types and cognitive processes of visual representations. As a result, new categories were added and the revised VRC-T was completed (VRC-TR). It was also found that both pre- and in-service teachers mainly focused on 'interpreting' the 'descriptive representation' while designing their lesson plans. Additionally, in-service teachers showed more limited use of visual representations compared to pre-service teachers. In-service teachers largely relied on the national science textbooks, while pre-service teachers reflected their own learning experiences in their teacher-training program. These results showed that teachers' use of visual representations heavily relied on their prior learning and teaching experiences. The VRC-TR presented in this study and examples of class activities in each category can be helpful for teachers and researchers who want to use visual representations more effectively.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.43-51
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• 2022

In this paper, we suggest the method to improve the existing method leaning context-free grammar(CFG) using tabular representation(TBL) as a chromosome of genetic algorithm in grammatical inference and show the more efficient experimental result. We have two improvements. The first is to improve the formula to reflect the learning evaluation of positive and negative examples at the same time for the fitness function. The second is to classify partitions corresponding to TBLs generated from positive learning examples according to the size of the learning string, proceed with the evolution process by class, and adjust the composition ratio according to the success rate to apply the learning method linked to survival in the next generation. These improvements provide better efficiency than the existing method by solving the complexity and difficulty in the crossover and generalization steps between several individuals according to the size of the learning examples. We experiment with the languages proposed in the existing method, and the results show a rather fast generation rate that takes fewer generations to complete learning with the same success rate than the existing method. In the future, this method can be tried for extended CYK, and furthermore, it suggests the possibility of being applied to more complex parsing tables.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.5-12
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• 2012

The information represented by graphic is preferred more than by text to the game generation familiar to computer games in the cognitive style. The learning to solve the math problems represented by graphic is significantly effective to improve learner's problem-solving power in math education. In this paper, we proposed a method of graphic representation of mathematical sentences for effective learning of the game generation. The proposed method arranges the unit informations in the logical structure and represent the logical interrelation between the informations by symbols, line segments, or arrows using the graphic elements with good visibility for the game generation to recognize easily and to understand accurately the logical meaning. The proposed method is able to represent accurately the math sentences until the detail level that appears the tense and the voice of the sentences differently from the previous graphic representation method's ability. The proposed method could be used as learning tools and used widely to represent graphically mathematical informations for the instructional scaffolding of an educational game in oder that the game generation could learn effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.2
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• pp.214-225
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• 2014

This study aimed to investigate the high school students' conceptions about the plate tectonics through visual representation. For this purpose, the subjects were 67 students in 11th-grade high schools in Chungbuk. In order to in-depth understand the students' conceptions about plate tectonics, so the investigator conducted a semi-structured interview. The conclusions were as in the following. After learning the plate tectonics, the students had the alternative conceptions associated with terminology, colors' meanings, plate-related melting, plate's movement, plates' boundaries, mantle's physical conditions, driving forces for plate movement, and they had the organic relations about colors' meanings, mantle's physical conditions, and driving forces of plate movement. Also, the visual representation used to teach plate tectonics influenced on the students' responses about terminology, plates' boundaries, plate-related melting and the mantle's physical features, also this study found the factors of visual representation causing the learners to create alternative conceptions. These results implicated the importance of teacher's role in identifying the students' interpretation process on visual representation, and it needed to improve the factors creating students' alternative conceptions about visual representation and to study the factors further.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.