• Proceedings of The KACE
• /
• 2018.01a
• /
• pp.21-23
• /
• 2018

테크놀로지의 중요성이 증가함에 따라 다양한 영역에 테크놀로지가 활용되고 있다. 교육에서도 효과적인 학습을 촉진하기 위하여 테크놀로지를 도입하고 있다. 하지만 테크놀로지에 대한 교사의 지식 부족으로 인하여 맹목적인 테크놀로지 활용이 이루어지고 있다. 이러한 문제를 해결하기 위하여 TPACK 연구가 활발하게 진행되고 있으며, 김성원과 이영준(2017)은 프로그래밍을 도입한 TPACK-P 교육 프로그램을 개발하였다. TPACK-P 교육 프로그램은 예비 교사의 Technological Pedagogical Knowledge (TPK), Pedagogical Content Knowledge (PCK), Technological Pedagogical Content Knowledge (TPACK) 발달에는 효과적이었지만, Technological Content Knowledge (TCK)에는 영향을 주지 않았다. 본 연구에서는 이러한 문제를 해결하기 위하여 프로그래밍 기반 수업 사례 탐색 및 분석을 보완한 TPACK-P 교육 프로그램을 개선하였다. 개선한 TPACK-P 교육 프로그램이 예비 교사의 TPACK에 미치는 영향을 살펴보기 위하여, K 대학에 다니고 있는 예비 교사 20명을 대상으로 개선한 TPACK-P 교육 프로그램을 적용하였다. 예비 교사의 TPACK 변화를 살펴보기 위하여 박기철과 강성주(2014)의 TPACK 검사 도구를 사용하였다. 이러한 실험을 통하여 TPACK-P 교육 프로그램은 예비 교사의 TPACK 향상에 효과적인 것을 확인할 수 있었다. 또한, TPACK의 모든 세부 영역의 발달에 효과적인 것을 확인할 수 있었다. 본 연구에서는 단일 집단을 대상으로 개선한 교육 프로그램을 적용하였다. 향후 연구에서는 대조군을 설정하고, TPACK-P 교육 프로그램이 예비교사의 TPACK에 미치는 영향을 비교 분석하여야 한다.

• Korean Journal of Child Studies
• /
• v.13 no.1
• /
• pp.5-15
• /
• 1992

아동의 언어습득 과정에서의 모방은 흔히 볼 수 있는 현상이지만 한편 인간의 독특하고 신기한 일면이기도 하다. 이 모방에 관하여 그동안 많은 연구들이 되어왔으나 그 중요성이나 역할에 대한 연구들의 결론이 모두 일치하는 것은 아니다. 본 논문에서는 이 모방현상에 대한 그 동안의 연구들을 이론별로 분류해 보고 각 이론들이 모방의 역할을 밝히기 위하여 사용해 온 연구방법, 절차들을 분석해 보았다. 행동주의와 사회학습이론에서는 언어습득에서의 모방의 역할을 특히 강조하고 있으며 이 모방의 효과를 입증하기 위하여 다른 어떤 이론보다도 훨씬 조직적인 실험연구를 해 왔다. 이와는 대조적으로, 언어 심리학적 접근에서는 언어습득에서의 생득성과 창의성을 강조하므로 모방의 역할은 중시하지 않으며, 모방의 정의를 엄격하게 규정하고 자연적인 관찰 중심의 연구를 주로 하여 언어습득에서의 모방의 역할이 미비하다는 결론을 내리고 있다. Piaget 중심의 인지발달적 접근에서는 모방을 인간의 전체 발달의 한 측면으로 보아 모방은 인지발달과 함께 점진적으로 발달되는 것으로 설명하고 있다. 특히 언어발달에서의 지연모방의 중요성을 강조하고 있으며, 대부분의 연구는 자연적인 관찰연구를 통하여 모방의 발달과정을 밝히고 있다. 언어발달에서의 모방의 역할에 대한 지금까지의 일치하지 않는 연구 결과들은 각각의 이론들이 나름대로 달리 모방을 정의하고, 언어의 다른 측면들을 다루어 왔기 때문으로 밝혀 졌다. 앞으로 언어발달이 아동의 발달 전체의 맥락 속에서 연구되어지고, 언어습득과정에서 보이는 아동들의 개인차까지도 고려되어지는 포괄적인 연구가 이루어지면 모방의 역할도 좀더 명백하고 일관성 있게 밝혀지리라 본다.

• Journal of Korean language education
• /
• v.25 no.1
• /
• pp.1-27
• /
• 2014

This study investigates how common English hedge expressions such as 'I think' and 'I guess' appear in Korean, with the aim of providing explicit explanation for English-speaking leaners of Korean. Based on a contrastive analysis of spoken English and Korean corpus, this study argues three points: Firstly, 'I guess' appears with a wider variety of modalities in Korean than 'I think'. Secondly, this study has found that Korean textbooks contain inappropriate use of registers regarding the English translations of '-geot -gat-': although these markers are used in spoken Korean, they were translated into written English. Therefore, this study suggests that '-geot -gat-' be translated into 'I think' in spoken English, and into 'it seems' in the case of written English and narratives. Lastly, the contrastive analysis has shown that when 'I think' is used with deontic modalities such as 'I think I have to', Korean use '-a-ya-get-': the use of hedge marker 'I think' with 'I have to', which shows obligation or speaker's volition turns the deontic modalities into expressions of speaker's opinion.

• Journal of Broadcast Engineering
• /
• v.24 no.4
• /
• pp.623-632
• /
• 2019

Many researches have been carried out for brightness and contrast enhancement, illumination reduction and so forth. Recently, the aforementioned hand-crafted approaches have been replaced by artificial neural networks. This paper proposes a convolutional neural network that can replace the method of generating a reflectance image where illumination component is attenuated. Experiments are carried out on 102 low-light images and we validate the feasibility of the replacement by producing satisfactory reflectance images.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.87-96
• /
• 2019

Improvement of low-light image mainly focuses on the contrast enhancement. Many researches have been carried out for brightness enhancement, contrast improvement and illumination reduction. Recently, the aforementioned approaches have been replaced by artificial neural networks. This paper proposes a methodology that can replace the Retinex-based reflectance image acquisition by deep neural network. Experiments carried out on 102 low-light images validated the feasibility of the replacement by producing PSNR=30.8682(db) and SSIM=0.4345.

• Journal of IKEEE
• /
• v.25 no.4
• /
• pp.750-755
• /
• 2021

This paper proposes techniques for detecting the damage status of each part of a vehicle using YOLOv4. The proposed algorithm learns the parts and their damages of the vehicle through YOLOv4, extracts the coordinate information of the detected bounding boxes, and applies the algorithm to determine the relationship between the damage and the vehicle part to derive the damage status for each part. In addition, the technique using VGGNet, the technique using image segmentation and U-Net model, and Weproove.AI deep learning model, etc. are included for objectivity of performance comparison. Through this, the performance of the proposed algorithm is compared and evaluated, and a method to improve the detection model is proposed.

• Journal of Ginseng Research
• /
• v.32 no.4
• /
• pp.341-346
• /
• 2008

In the present study, we assessed the effects of white ginseng and red ginseng extract on the learning and memory impairments induced by scopolamine. The cognition-enhancing effect of ginseng extracts was investigated using the Morris water maze and Y-maze test. Drug-induced amnesia was induced by treating animals with scopolamine (2 mg/kg, i.p.), an antagonist of muscarinic acetylcholine (ACh) receptor. Tacrine was used a positive control. Ginseng extract (200 mg/kg, p.o.), tacrine (10 mg/kg, p.o.) administration significantly reduced the escape latency during training in the Morris water maze (p<0.05). At the probe trial session, scopolamine significantly increased the escape latency on day 5 in comparison with control (p<0.01). The effect of ginseng extracts on spontaneous alternation in Y-maze was similar to that of scopolamine treated group. In addition, numbers of arm entries were similar in all experimental groups. Moreover, red ginseng extract significantly inhibited acetylcholinesterase activity in the cortex and serum (p<0.05). Brain ACh contents of ginseng extract treated groups increased more than that of scopolamine group, which did not show statistically significant. These results suggest that ginseng extract may be useful for the treatment of cognitive impairment.

• Journal of The Korean Association For Science Education
• /
• v.35 no.1
• /
• pp.1-14
• /
• 2015

The purpose of this study is to examine North Korean defector students' characteristics in science learning through their voice in an "Angbuilgu" program, one of the Korean traditional science knowledge (TSK). We compared them with two other groups of contrasting backgrounds. The Angbuilgu program contains meaningful questions of time, everyday-life knowledge, Korean TSK, and western modern science (WMS). The teaching strategy consists of interactions between teacher and students, and scientific experiments. We applied this program to three groups and analyzed: North Korean defector students, elementary science gifted students, high school students in an advanced class. The characteristics of their science learning show the following: First, their interpretation of time as nature itself in their everyday life. They have rich experience and are familiar with time in nature. Second, they prefer science with complementary, caring, and humanist perspectives, which is in contrast to other groups with preference to the updated and practical science. Third, they lack scientific concepts but possess an abundance of everyday-life knowledge. Their linguistic expressions are ordinary rather than scientific. Fourth, they are familiar with narrative thinking more than scientific thinking. The results show that the science program using Korean TSK can help them accept new scientific knowledge as well as cultural pride, which plays a role in reconfirming their identity as one ethnicity. We expect that the contents of Korean TSK can be an intercultural field between North Korean defector students and our science curriculum.

• Journal of the Korean earth science society
• /
• v.40 no.1
• /
• pp.86-106
• /
• 2019

The purpose of this study is to develop an astronomy education program reflecting astronomical thinking to be used at science museum and to investigate the effect of the program on the improvement of astronomical thinking ability of high school students. After selecting the components of astronomical thinking through literature studies, we developed an astronomy education program consisting of four stages: demonstration and observation, and question and thinking, support and group discussion, demonstration and assessment. In order to verify the effectiveness of the program, we conducted a covariance analysis on the pre- and post-tests of the experimental group and control group to examine the level of students' thinking before and after using the program in teaching and learning. As a result, it was confirmed that the astronomy education program reflecting astronomical thinking was effective in promoting students' astronomical thinking ability. In particular, this program was effective in enhancing the ability of modeling by reconstructing the observed astronomical phenomenon from the viewpoint of the universe with respect to spatial thinking in the astronomy domain. It was also effective to improve the ability of organizing the system by grasping the relationship between the elements constituting the astronomical system in relation to the system thinking in the astronomy domain. This study is significant in suggesting a specific teaching and learning program to develop students' astronomical thinking.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.10
• /
• pp.455-460
• /
• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.