• Journal of Science Education
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• v.41 no.2
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• pp.195-212
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• 2017

Small group learning is an educational approach to allow students to solve the problems and to achieve a common goal. Especially, small group learning in science education is one of the most important educational approaches and effective to ensure understanding of a topic. Small group learning consisting of three students in science education maximize student understanding and learning efficiency. However, It is reported that the effects of small group learning on achievement show different results, corresponding to different grouping methods(homogeneous/heterogeneous). This study investigated the effects of grouping method on difference of ecological niche of photosynthesis concepts. To achieve this, 1107 7th students were composed of homogeneous and heterogeneous groups classified into top, middle, and bottom levels. The photosynthesis units were divided into four categories: the photosynthesizing place, the substances of photosynthesis, required materials for the photosynthesizing, and environmental factors affecting photosynthesis. A questionnaire was composed by selecting concepts having a frequency of 4% or more based on prior studies on the change of the ecological status of photosynthesis. The questionnaire was scored in terms of relativity and understanding on each of the proposed concepts in the four categories. The result of this study is as set forth below. 1) There was an enhancement of learning the concept of science in small group classes consisting of 3 students. 2) To enhance the average upon composing of a group, it is proposed that the group should be formed homogeneously, and to reduce the deviation between the members, it is proposed that the group should be formed heterogeneously. Through this study, it is expected that specific studies verifying the difference or effect on the duplicity of results are conducted based on the composition of groups.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.399-413
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• 2020

In this study, we analyzed science teachers' NOS-PCK in Science Inquiry Experiment lessons. Four science teachers in charge of Science Inquiry Experiment in high schools located in the Seoul metropolitan area participated in the study. NOS Lessons were observed, all of the teaching-learning materials were collected, and semi-structured interviews were conducted. All the collected data were analyzed according to five factors of NOS-PCK. As a result of the study, their understanding and consideration of the curriculum related to NOS were insufficient in some cases. They thought that given inquiry activities or textbook composition was not effective for NOS teaching so that they actively reconstructed the curriculum. In terms of teaching strategies, their lessons were close to explicit approaches. However reflective approaches were generally lacking. They were neglected in evaluating NOS for reasons that views of NOS are individually subjective or that NOS is not an area of cognitive learning. They guessed the state of students by relying on their own experiences rather than based on evaluation results. They recognized a specific aspect of values of NOS learning. And intention to teach NOS played an important role throughout their classes. Based on the above results, we discuss some ways to improve the professionalism of science teachers for NOS teaching.

• The Mathematical Education
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• v.44 no.4 s.111
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• pp.603-625
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• 2005

Identity is the concept which approaches individuals' affective problems with the social and cultural view. The previous studies on the problems, studied the attitudes, beliefs, or emotions while they restricted the problems to teachers or students' private problems. Otherwise, identities focus on individuals which participate to any community and share its social practices(Mclead, 1994). This study purposed to get an understanding on the teaching and learning mathematics in elementary mathematics classroom with an ethnographic view, while we consider mathematics as a kind of social practices, and mathematics classrooms as communities of practice. We analysed teacher's identities on mathematics and teaching mathematics depending on her responses of the questions as following: How does she think about mathematics, what are the instructional goals in her mathematics classroom, how do students learn mathematics in her mathematics classroom. In addition, we analysed students' identities on mathematics and learning mathematics depending on their responses of the questions as following: What do students think of mathematics, do they like mathematics, why do they study mathematics, how do they feel their mathematics classroom(describe your classroom) and themselves in it(describe yourselves in your classroom), what are their duties and what do they do actually in their mathematics classroom.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Transactions of the Korean hydrogen and new energy society
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• v.32 no.6
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• Journal of Engineering Education Research
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• v.25 no.3
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• pp.26-34
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• 2022

Students' questions are essential and important for learning, but previous research and experience of instructors shows that there is a lack of interaction between instructors and students in online classes. This research studies how learners can effectively ask questions in online classes at engineering colleges. Based on two axes of anonymity and openness, the four different types of questioning methods were suggested as 'onymous/public', 'onymous/private', 'anonymous/public' and 'anonymous/private.' In this study, seven communication channels were applied to check their effectiveness in an online class. The results showed that learners' satisfaction with learning outcomes increased compared to previous offline classes, while satisfaction with teaching methods was similar. Additionally, among the four types of questioning methods, the preference and effectiveness of 'anonymous/public' was highest, followed by 'onymous/private'. This study suggests several implications of educational approaches to online education in engineering colleges.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.259-270
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• 2022

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.