• Journal of Educational Research in Mathematics
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• v.12 no.4
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• pp.473-491
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• 2002

This article analyzes mathematics education from dialectical materialism acknowledging the objectivity of knowledge. The thesis that knowledge is objective advances to the recognition that knowledge will be internalized, and an idea of zone of proximal development(ZPD) is established as a practice program of internalization. The lower side of ZPD, i.e. the early stage of internalization takes imitation in a large portion. And in the process of internalization the mediational means play an important role. Hereupon the role of mathematics teacher, the object of imitation, stands out significantly. In this article, treating the contents of study as follows, I make manifest that teaching and learning in mathematics classroom are united dialectically: I hope to findout the method of teaching-learning to mathematical knowledge from the point of view that mathematical knowledge is objective; I look into how analysis into units, as the analytical method of Vygotsky, has been developed from the side of mathematical teaching-learning; I discuss the significance of mediational means to play a key role in attaining the internalization in connection with ZPD and re-illuminate imitation. Based on them, I propose how the role of mathematics teachers, and the principle of organization to mathematics textbook should be.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Journal of Creative Information Culture
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• v.7 no.2
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• pp.79-90
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• 2021

The purpose of this paper is to study the unplugged educational program for the lower grades of elementary school. For this, the study was conducted as follows. First, a play-learning-based unplugged education method was discovered, focusing on play activities according to the level of development of elementary school students. Secondly, unplugged educational programs to develop Computational Thinking were designed according to the discovered topics. each class is conducted by storytelling, and the content of the storytelling is related to the integrated curriculum 'Winter'. In addition, each class was analyzed based on the core elements of Computational Thinking ability. And, we developed educational materials that can be used in the designed unplugged educational program. Finally, the educational program was applied to the lower grades of elementary school, and the educational program was analyzed through case studies. As a result of the analysis, the educational program was organized according to the level of the students, and it was confirmed that this educational program is helpful in improving the Computational Thinking of lower grade students of elementary school.

• Cartoon and Animation Studies
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• s.32
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• pp.363-381
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• 2013

In this paper, on the web, offline mobile learning content to reinforce the learning of the video frame-by-frame necessary for re-learning area to capture only the important areas. The frame of the captured image and the image in the form of advanced training time saved and also a description of the notes feature to store. The area needed for the capture area re-learning the learner to learner-centered custom systems can be applied. In order to capture the learning program, regardless of the configuration of the selected frame by frame in order to capture the user-centric storytelling-based learning can be applied. Capture the full effect of the system compared to learning and learner-centered learning time-saving reconstruction of the frame according to the customized learning to play a positive role in improving effectiveness.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.177-187
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• 2018

The purpose of this study is to investigate the influence of learning flow, learning interest, and academic achievement by dividing the time when class was taught by Havruta. The Havruta teaching method is a traditional Jewish method of learning, with a one-on-one discussion with a partner that has a positive impact on each other. Havruta teaches learners through various perspectives and perspectives, helping them to improve their learning ability by attracting new ideas and solutions. In the computer lab, there is a big difference between the students according to the learner's abilities. Therefore, it is thought that the Havruta teaching method will help the learners who have lost interest in learning and improve the learning ability in the conventional way which does not consider personal abilities. do. In this paper, based on the friendship teaching model of the Havruta teaching style, the experimental group was taught through the Havruta practice and the play. Through the pre- and post-test, the students who taught the class with the help of the verbal method improved the learning flow, the learning interest and the academic achievement.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.44-52
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• 2022

With recent scientific advances and growing interest in AI technologies, AI-based chatbots have been viewed as a practical learning aid for English language development. The purpose of this study is to examine preservice teachers' perceptions on the potential benefits of employing AI chatbots in English instruction and its pedagogical aspects. 28 preservice teachers majoring in English education were asked to use Kuki chatbots for a week with a guidance of a researcher and then report on their perceptions of AI chatbots in terms of perceived usefulness after use, applicability, and educational benefits and drawbacks. Emerging codes and themes were identified and evaluated using Thematic Analysis(TA) based on qualitative data from surveys and interviews. The findings show that six emerging themes were identified, encompassing perspectives on teacher, learner, communication, linguistic, affective, and assessment. The overall findings of this study revealed that AI-based chatbots can play a significant role as learning tools for stimulating interactive communication in a target language. Most preservice primary teachers acknowledge that AI chatbots can be useful as teaching and learning aids for both teachers and students. Furthermore, when applying various learner data to chatbot technology, such as learner assessment and diagnosis, a guided approach is necessary to perform a conversation appropriate for the learner's level and characteristics. Finally, as chatbots have a variety of benefits in terms of affective aspects, they may improve EFL learners' confidence in speaking English and learning motivation.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.117-125
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• 2009

This paper describes the result of applying neuro-fuzzy reasoning, which conducts Go term knowledge based on pattern knowledge, to the opening game of Go. We discuss the implementation of neuro-fuzzy reasoning for deciding the best next move to proceed through the opening game. We also let neuro-fuzzy reasoning play against TD($\lambda$) learning to test the performance. The experimental result reveals that even the simple neuro-fuzzy reasoning model can compete against TD($\lambda$) learning and it shows great potential to be applied to the real game of Go.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.1001-1007
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• 2024

Recent deep learning algorithms for object detection in real-time play a crucial role in various applications such as autonomous driving, traffic monitoring, health care, and water quality monitoring. The size of small objects, in particular, significantly impacts the accuracy of detection models. However, data containing small objects can lead to underfitting issues in models. Therefore, this study developed a deep learning model capable of quickly detecting small objects to provide more accurate predictions. The RE-SOD (Residual block based Small Object Detector) developed in this research enhances the detection performance for small objects by using RGB separation preprocessing and residual blocks. The model achieved an accuracy of 1.0 in image classification and an mAP50-95 score of 0.944 in object detection. The performance of this model was validated by comparing it with real-time detection models such as YOLOv5, YOLOv7, and YOLOv8.