• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.157-165
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• 2020

As interest in software education has increased, discussions on teaching, learning, and evaluation method it have also been active. One of the problems of software education teaching method is that the instructor cannot grasp the content of coding in progress in the learner's computer in real time, and therefore, instructors are limited in providing feedback to learners in a timely manner. To overcome this problem, in this study, we developed a software education support system that grasps the real-time learner coding situation under block-based programming environment by applying a learning analysis technique and delivers it to the instructor, and visualizes the data collected during learning through the Hadoop system. The system includes a presentation layer to which teachers and learners access, a business layer to analyze and structure code, and a DB layer to store class information, account information, and learning information. The instructor can set the content to be learned in advance in the software education support system, and compare and analyze the learner's achievement through the computational thinking components rubric, based on the data comparing the stored code with the students' code.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Journal of Korean Biological Nursing Science
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• v.3 no.2
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• pp.63-68
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• 2001

Increasing interest in computer-mediated learning technologies has prompted educators to incorporate them into many learning environments ; however, there is still little evaluative evidence to support their effectiveness. This report describes the development and evaluation of a web-based instructional program on basic nursing science for nursing students. Researcher-designed questionnaires were used to assess the characteristics of our students, and to solicit their ratings of the instructional program on ease of use, accuracy of content, clarity of content, interest, and convenience of the program, using 5-point Likert scales. The respondents indicated that the package was easy and convenient to use, with high technical quality, and of a level challenging to some but not all of the students. On-line quizzes were most highly rated. Also it was confirmed that frequent users of electronic bulletin board showed much higher achievement score than that of nonfrequent users. It was also found that the effect of cyber education was dependent on the active participation of the students. These data suggest that the use of web-based instructional program as a distance education strategy can be an effective method for nursing students and nurses.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.95-99
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• 2023

Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.

• 대한공업교육학회지
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• v.36 no.2
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• pp.1-23
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• 2011

The purpose of the study was to investigate the generality and applicability of Team-Based Learning model in a technical high school, based on the interviews with students and a teacher. Team-Based Learning model seems to be an effective way in improving the performance of groups as well as the individualized learning and team interaction. We applied a formative research method and identified the strengths of the model including learners' motivation and interests, learner-centered learning, self-efficacy through learning in advance, and concept acquisition from the repetitive learning process. However, we also found the weakness of the model including impracticality of instructional design, a lack of field-oriented problem banks, and needs for identifying learner characteristics and role in instruction. Finally, we analyzed the implications for the Team-Based Learning in the technical high schools in light of team formation, discussion types, active participation, and learners' prior knowledge and attitude, and pre-determined instructional design.

• Journal of Korean Home Economics Education Association
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• v.34 no.3
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• pp.101-116
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• 2022

This study aims to apply the problem-based learning method to a college course on 'Consumer Studies' in a teacher education program. The participants of this study were 27 undergraduate students who were enrolled in the course. The PBL class was implemented for seven weeks using two problems: proposing special lectures on economic concepts and financial consulting. The effectiveness of PBL was analyzed through self-reflective journals, self-assessment, and a learning satisfaction survey of students. The result of this study was as follows. Students achieved the learning objectives and strengthened their collaboration and communication skills with team members during the PBL process. Moreover, students improved their self-directed learning and problem-solving ability through the PBL class. The results of self-assessment, in terms of learning task performance, active interaction, and self-directed learning were generally high at 4.63 points. In addition, the overall learning satisfaction level was very high, with a score of 4.75. The results will provide effective information on PBL classes to instructors and prospective teachers and will be used as data for learner-centered PBL classes.

• Journal of The Korean Association For Science Education
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• v.26 no.3
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• pp.393-405
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• 2006

The purpose of this study was to analyze the effects of Semantic Network Program (SNP) instruction on learning achievement and motivation in high school biology classes. For this study, a SNP was designed by applying the recommendations in regard to student attention and satisfaction factors in Keller's ARCS theory. SNP instruction was conducted with an experimental group and a control group, each consisting of 62 high school biology class student. A pretest-posttest control group design was employed. The pre-test was used to analyze the learning achievement test, learning motivation test, and semantic forming test. For 4 weeks the experiment group was instructed using the developed SNP which centered on Keller's attention and satisfaction factors, and the control group was instructed via teacher-centered lectures based on the textbook. It was found that SNP instruction efficiently increased students' biology learning achievement (p<.001). It was also discovered that SNP instruction was effective in increasing Keller's motivation strategies on attention and satisfaction factors (p<.001). In addition, SNP instruction positively affected students' semantic formation (p<.001) and learning content retention (p>.05) in the heredity unit by aiding students in the area of active multimedia learning. An in depth interview with students in the class using SNP instruction showed that material learned via this method in biology had longer retention of problem-solving methods. Consequently, SNP instruction according to motivation strategies may high school biology teachers with meaningful teaching-learning methods strategies for the unit on heredity.

• The Mathematical Education
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• v.56 no.1
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• pp.101-118
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• 2017

In this article we study a reconstruction of mathematical knowledge on trigonometry by the method of ascent from the abstract to the concrete from the pedagogical viewpoint of dialectic. The direction of education is shifting in a way that emphasizes the active constitution of knowledge by the learning subjects from the perspective that knowledge is transferred from the teacher to the student. In mathematics education, active discussions on the construction of mathematical knowledge by learners have been going on since the late 1990s. In Korea, concepts and aspects of constructivism such as operational constructivism, radical constructivism, and social constructivism were introduced. However, examples of practical construction according to the direction of construction of mathematical knowledge are very hard to find. In this study, we discuss the direction of the actual construction of mathematical knowledge and suggest a concrete example of the actual construction of trigonometry knowledge from a constructivist point of view. In particular, we discuss the process of the construction of theoretical knowledge, the ascent from the abstract to the concrete, based on the literature study from the pedagogical viewpoint of dialectic, and show how to construct the mathematical knowledge on trigonometry by the method of ascent from the abstract to the concrete. Through this study, it is expected to introduce the new direction and new method of knowledge construction as 'the ascent from the abstract to the concrete', and to present the possibility of applying dialectic concepts to mathematics education.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.