• Journal of Information Technology Applications and Management
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• 제24권4호
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• pp.57-69
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• 2017

Due to the development of technology even in learning and education area, many studies have begun to make a new attempts to research by using SNS, breaking away from traditional learning methods. However, the limitations of these studies are restricted only to the use of wireless Internet and writing on Web sites. This study aims to conduct a research on English learning activities that utilize various technologies such as Bigdata, Facebook, Social Network Services (SNS) and English applications. In addition, this study looks into how these modern technologies can be integrated in the classrooms and which activities can be applied in the English classroom. This research is to suggest effective English learning methods through a thorough investigation on the effectivity of various technologies based on the Web 2.0 such as Flickr, blogs, MySpace, and online discussion board within the context of the English learning. To verify the effect of the study, the subjects are divided into experimental and control group. The experiment is proceeded with pre- and post-test. The experimental group is designed to verify the effects using SNS tools such as Facebook, Bigdata, and Online Massive Learning. A survey is conducted to determine the preference of utilizing social networking sites and to analyze the effects in class. The result is that the average scores for experimental group have improved more than the average of control group. The comparison of pre and post-test of the experimental group shows that the significance of the higher and median group was statistically significant at the p<0.01.

• 수산해양교육연구
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• 제28권5호
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• pp.1478-1491
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• 2016

This study has designed to find out the effects of a project-based flipped learning model at universities on the self-directed learning ability, the self-leadership, and the learning competency. For the study, two procedures were performed. First, a flipped learning model for a project-based learning was developed on the basis of the literature reviews. The flipped learning model has three different steps: the pre-class, the in-class, and the post-class. In the pre-class, instructors provide mini-core courses using various technologies for learners outside the class. The in-class is the step to check whether learners prepare their learning or not. Also, in this step, the in-death learning and the teaching-learning process by interaction between instructors and learners would be performed. In the post-class, learners would be able to sustain the extended learning to develop the learning tasks and activities after flipped learning class. Through this step, the learners could be experienced integrated thinking and application, documentation and management, as well as sharing and spread of their learning. Second, the effectiveness of the developed flipped learning model on the self-direction, the self-leadership, and the learning competency was examined. The quantitative research method and the qualitative research method were used for this study. The results indicated that the flipped learning model showed improvement on the self-direction, the self-leadership, and the learning competency.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.11-17
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• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• 한국초등과학교육학회지:초등과학교육
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• 제26권5호
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• pp.489-498
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• 2007

This study focuses on surveying and examining the beliefs of elementary pre-service and in-service teachers about science and science education. The instrument consisted of 21 items about science and science education on a 5-Likert scale(score range from 1 to 5). The one contained science knowledge and scientific invention, and the other contained science teacher, learning science and science learning and teaching. Data were collected from 76 pre-service and 96 in-service elementary teachers(24 male and 148 female). The elementary pre-service and in-service teachers had higher level belief about that science knowledge should be acquired by sequential scientific process, the beliefs of in-service teachers was more explicit than those of pre-service teachers. They had beliefs to educate learners by providing scientific joyfulness and sequential scientific process. But, in-service teachers had difficulties to perform scientific process-based activities. It is necessary to provide scientific experiences to understand the nature of science in pre-service and in-service programs.

• 한국콘텐츠학회논문지
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• 제14권4호
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• pp.480-490
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• 2014

본 연구에서는 예비 기독영어교사들이 전공봉사학습의 일환으로 서울 소재 두 교회의 주일학교 중학생들을 대상으로 개발한 영어성경학습 교재를 실제 교육상황에 적용해 본 후, 예비 영어교사들의 교재개발 과정에 대한 인식과 주일학교 교사들의 교재에 대한 인식이 어떠한지를 살펴보고자 하였다. 본 연구에는 영어성경학습 교재 개발 과정이 단계별로 기술되어 있다. 본 연구의 자료 분석을 위해 예비 영어교사들과 주일학교 교사들과의 심층 인터뷰를 실시하였다. 인터뷰 내용은 녹음하여 전사하였고 내용분석 방법을 통해 분석하였다. 연구 결과, 예비 영어교사들의 영어성경학습 교재 개발 경험을 통한 전공봉사학습이 전공 전문성 향상은 물론 교회를 돕는 보람과 만족감을 갖게 한 것으로 나타났다. 성경학습 교재 내에서 성경교육과 영어교육 목표 사이의 균형을 이루는 것은 예비 영어교사들과 교재를 사용해 본 주일학교 교사들 모두에게 어려운 점으로 드러났다. 마지막으로, 주일학교 교사들은 교재 내의 학습활동들의 연계가 자연스럽지 못한 점을 한계로 인식하였으나, 교재의 소재가 학생들의 실생활과 밀접하게 연관되어 있는 점은 긍정적으로 인식하고 있었다.

• 한국학교수학회논문집
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• 제15권2호
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• pp.259-279
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• 2012

본 연구의 목적은 개념연결표의 활용이 예비교사들의 수학 학습에 미치는 영향에 대해서 조사하는 것이다. 본 연구는 25명의 예비교사들을 대상으로 미적분학 강좌 시간을 활용하여 한 학기 동안 수행되었다. 연구에 참여한 예비교사들을 대상으로 실시한 설문조사 및 면담 결과에 의하면 개념연결표의 활용은 여러 가지 측면에서 예비교사들에게 긍정적인 영향을 미친 것으로 나타났다. 개념연결표의 활용은 예비교사들의 수학적 개념에 대한 이해와 수학적 의사소통능력을 발달시키는데 도움이 되었으며 수학의 유용성이나 가치 인식 및 자기주도적이고 적극적인 수업 참여를 유도하는데 효과적인 것으로 나타났다.

• East Asian mathematical journal
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• 제39권2호
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• pp.93-117
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• 2023

The aim of this study is to explore the pre-service math teachers' characteristics of education to develop their AI literacy and SW competency, and to derive some implications. We conducted a 14-hours AI and SW education program for pre-service teachers with theory and practice, and an analysis on class observation data, video frames of classes and interview, Python programming assignments and papers. The results of this case study for 3 pre-service teachers are as follows. First, two students understood artificial neural network and deep learning system accurately, furthermore, all students conducted a couple of explorations related with performance improvement of deep learning system with interest. Second, coding and exploration activities using Python improved students' computational thinking as well as SW competency, which help them give convergence education in the future. Third, they responded positively to the necessity of AI literacy and SW competency development, and to applying coding to math class. Lastly, it's necessary to endeavor to give a coding education to the student's eye level according to his or her prerequisite and to ease the burden of student's studying AI technology.

• 과학교육연구지
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• 제47권3호
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• pp.263-272
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• 2023

예비교사가 비유클리드 기하학에서 수학적 정의를 이용한 이차곡선의 학습으로 유클리드 기하학의 다양한 개념을 어떻게 이해하고 활용할 수 있는지를 살펴본다. 본 연구에서는 D 대학교 수학교육과 3학년 수업에서 수학적 정의를 이용하여 택시기하, 민코프스키 거리공간과 같은 비유클리드 공간의 이차곡선 학습이 예비교사들에게 새로운 기하학적 개념을 습득하고 수용하는 능력 향상에 도움을 줄 수 있음을 보였다. 이러한 결과로부터 택시기하와 민코프스키 거리공간에서의 정의를 활용한 이차곡선 학습이 창의적이고 유연한 사고를 유도하여, 예비교사들의 유클리드 기하학 교육 전문성 향상에 기여할 것으로 기대된다.

• 기본간호학회지
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• 제23권4호
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• pp.430-439
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• 2016

Purpose: The purpose of this study was to contribute to the development of an efficient teaching-learning method by analyzing effects of writing reflective journals on meta-cognition and problem solving ability in nursing students in education applying blended learning for fundamental nursing skills. Methods: The research design was a one-group pretest-posttest design, done to assess changes in meta-cognition and problem solving ability. Participants were 63 nursing students taking the fundamental nursing skills course at one college in Gyeonggi Province. The course was offered from March 21 to June 3, 2016. Data were collected using pre and post tests given before and after writing of reflective journals in blended learning. Data were analyzed using t-test, ANOVA, $Scheff{\acute{e}}^{\prime}s$ test and paired t-test with SPSS Statistics version 20.0. Results: The results of this study show that scores for meta-cognition and problem solving ability of these students were all above average. There was a statistically significant difference in meta-cognition between pre and post writing of reflective journals but not for problem-solving ability. Conclusion: The findings indicate that writing a reflective journal in blended learning is an efficient teaching-learning method to improve meta-cognition in nursing students.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.