• The Journal of Korean Association of Computer Education
• /
• v.7 no.6
• /
• pp.59-68
• /
• 2004

Cooperative web-based learning is an teaching strategy in which small teams, each of students with different levels of ability, use a variety of learning activities to improve their understanding of a subject via the web. The objective of this paper is to propose new assessment items for evaluating students fairly in cooperative web-based learning. As a result, improved academic achievement, improved behavior and attendance, and increased self-confidence can be made in cooperative web-based learning due to the fair assessment, In this paper, the environment and instructional strategies for successful learning are firstly examined. In addition, the existing evaluation items in traditional classroom are also analyzed in order to develop new evaluation criteria in the web. Based on these analyzed items, we propose new evaluation items for cooperative web-based learning. In addition, the proposed items related to participant ratio, cooperability, and accountability are analyzed according to team organization styles and instructional models.

• Journal of the Korean School Mathematics Society
• /
• v.10 no.1
• /
• pp.129-148
• /
• 2007

Recent reform movement in mathematics education has focused not only on the curriculum development but also on teachers' learning or professional development. Whereas various theoretical paradigms call for different programs of professional development for teachers, one of the common emphases is on the pedagogical content knowledge [PCK] which encompasses contents and methods to teach. Against this background, this study developed comprehensive instructional materials for the purpose of fostering PCK in mathematics for elementary school teachers with 17 essential learning themes such as fraction, plane geometry, and area. Each loaming theme was first summarized on the basis of literature reviews and surveys in terms of knowledge in mathematics contents, knowledge in teaching methods, and knowledge in students' mathematical understanding and learning. Each theme was then analyzed in detail on how it was represented in the national curriculum and its concomitant textbooks along with workbooks. Finally, this report included a reconstruction of one unit in textbooks per each learning theme, followed by teaching notes and suggestions from classroom implementation. This was intended for teachers to apply what they might loam from this material to their actual mathematics instruction. Given the page limit, this paper dealt only with the learning theme of ratio.

• Smart Media Journal
• /
• v.10 no.1
• /
• pp.32-38
• /
• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Human Ecology Research
• /
• v.61 no.4
• /
• pp.543-563
• /
• 2023

The purpose of this study is to design and develop a Metaverse ZEP platform-based teaching and learning process plan by selecting learning topics that are commonly dealt with among the core concepts of the "family" area of practical (technical and home) subjects. To this end, a teaching and learning process plan was developed through planning, Metaverse platform design, expert review, and revision stages. The Metaverse ZEP "Open Class Day" platform, a virtual learning space, was created and developed to further utilize EduTech programs, such as Padlet, Mentimeter, Jamboard, Miricanvas, and Spatial. The teaching and learning process plan developed in this study consists of a total of seven sessions, including approaching EduTech, Changing Families, Exploring Our Family, and Counseling Centers 1, 2, and 3. Among them, Geumji Counseling Center 1, 2, and 3 was designed as a class in which parents and children participate together in open classes using the ZEP platform. This platform can be used as part of parent classes as well as to encourage online participation in the open classes held periodically at each individual school. In terms of the content validity ratio (CVR) of the developed teaching and learning process verified through five experts, 12 out of 15 questions had a CVR of 1, while the remaining three questions had a CVR of 0.6. The three questions with lower validity were revised and supplemented.

• Korean Journal of Child Studies
• /
• v.38 no.1
• /
• pp.117-126
• /
• 2017

Objective: The purpose of this study was to investigate young children's nonsymbolic magnitude comparison ability according to ratio limit and task condition. Methods: The participants included 40 3-year-old children, 42 4-year-old children, and 41 5-year-old children recruited from 4 childcare centers located in Seoul, Korea. All magnitude comparison tasks were composed of image material tasks and concrete material tasks. In addition, each magnitude comparison task varied with the ratio of the two quantities; 0.5 ratio, 0.67 ratio, 0.75 ratio. Results and Conclusion: The results revealed that 3-, 4-, and 5-year-old children could perform nonsymbolic magnitude comparison tasks without learning experiences. Also, 3-, 4-, and 5-year-old children could perform concrete material tasks better than image material tasks in nonsymbolic magnitude comparison tasks. Furthermore, children's performance on nonsymbolic magnitude comparison tasks indicated the ratio signature of the approximate number system. Children have a degree of numerical capacity prior to formal mathematics instruction. Also, children were influenced by task conditions or sense stimulus when they processed numerical information. Furthermore, the approximate number system can be used in understanding the ordinality of number.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1318-1321
• /
• 1997

A method for two-dimensional position measurement using an enclosing field has been studied and reported. The feature of this mehtod is zooming functional measurement by operating both the initial phase shift and the brightness ratio of the lighting function. An experimental system was developed and the experimental results on zooming effects are shown in this paper. This system is also an example of a "progressive learning measurement system".tem".uot;.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.27-33
• /
• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.45-52
• /
• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.2
• /
• pp.48-55
• /
• 1998

Structural learning methods of MLP classifiers for a given application using genetic algorithms have been studied. In the methods, however, the search space for an optimal structure is increased exponentially for the physical application of high diemension-multi calss. In this paperwe propose a method of MLP classifiers using species genetic algorithm(SGA), a modified GA. In SGA, total search space is divided into several subspaces according to the number of hidden units. Each of the subdivided spaces is called "species". We eliminate low promising species from the evoluationary process in order to reduce the search space. experimental results show that the proposed method is more efficient than the conventional genetic algorithm methods in the aspect of the misclassification ratio, the learning rate, and the structure.structure.

• Structural Engineering and Mechanics
• /
• v.57 no.4
• /
• pp.763-783
• /
• 2016

A methodology based on Teaching Learning-Based Optimization (TLBO) algorithm is proposed for optimum design of reinforced concrete retaining walls. The objective function is to minimize total material cost including concrete and steel per unit length of the retaining walls. The requirements of the American Concrete Institute (ACI 318-05-Building code requirements for structural concrete) are considered for reinforced concrete (RC) design. During the optimization process, totally twenty-nine design constraints composed from stability, flexural moment capacity, shear strength capacity and RC design requirements such as minimum and maximum reinforcement ratio, development length of reinforcement are checked. Comparing to other nature-inspired algorithm, TLBO is a simple algorithm without parameters entered by users and self-adjusting ranges without intervention of users. In numerical examples, a retaining wall taken from the documented researches is optimized and the several effects (backfill slope angle, internal friction angle of retaining soil and surcharge load) on the optimum results are also investigated in the study. As a conclusion, TLBO based methods are feasible.