• Journal of Music and Human Behavior
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• v.3 no.1
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• pp.63-76
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• 2006

Melody is defined as adding spatial dimension to the rhythm which is temporal concept. Being able to understand melodic pattern and to reproduce the pattern also requires cognitive skills. Since 1980, there has been much research on the relationship between academic skills and music cognitive skills, and how to transfer the skills learned in music work to the academic learning. The study purported to examine various research outcomes dealing with the correlational and causal relationships between musical and academic skills. The two dominating theories explaining the connection between two skills ares are "neural theory" and "near transfer theory." The theories focus mainly on the transference of spatial and temporal reasoning which are reinforced in the musical learning. The study reviewed the existing meta-analysis studies, which provided evidence for positive correlation between academic and musical skills, and significance of musical learning in academic skills. The study further examined specific skills area that musical learning is correlated, such as mathematics and reading. The research stated that among many mathematical concepts, proportional topics have the strongest correlation with musical skills. Also with reading, temporal processing also has strong relationship with auditory skills and motor skills, and further affect language and literacy ability. The study suggest that skills learned in the musical work can be transferred to other areas of learning and structured music activities may be every efficient for children for facilitating academic concepts.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.22-33
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• 2010

The main purpose of this study was to develope a reflective journal and examine its effects on student's academic achievement and self-regulated learning strategies. For this study, 'a structured reflective journal' was designed through the steps of systems approach with the purpose of enhancing student's academic achievement and self-regulated learning strategies, especially meta-cognition and critical thinking. The reflective journal used in this study contained the constructive elements of (1) self-evaluation with 5 likert scale, (2) learning essay, (3) dialogue with peers, and (4) dialogue with professor. A total of 94 freshmen enrolled in one of two sections of the engineering courses(theory-based class and experiment and practice-based class) participated in the study for 8 weeks. A pre-test-post-test design was used to examine the effects of the application of reflective journal on student's achievement and self-regulated learning strategies. For the result, analysis of covariance was conducted to determine whether there were any academic achievement differences and self-regulated learning strategy differences. The results suggested that students were taking advantages of the reflective journal, and there were statistically significant differences in academic achievement in the experiment and practice-based class and self-regulated learning strategies in both classes.

• Journal of The Korean Association of Information Education
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• v.18 no.3
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• pp.413-422
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• 2014

The positive impacts of robotics education and programming education on learners are similar. However, robotics education differs from programming education because it includes purchasing and building robots that cause financial and cognitive load of learners. Due to these differences, two kinds of education may not possess equal efficacies for all schools or all learning objectives. To verify this hypothesis, we conducted meta-analysis of studies on robotics education published in South Korea to estimate the effect sizes and compare it to that of programming education. The difference between the average effect sizes of robotics education and of programming education was significant, as the former was 0.4060 and the latter 0.6664. The average effect size of programming education was significantly larger than that of robotics education for primary school students. Middle school students achieved the highest results in both robotics education and programming education. Also, robotics education became more effective than programming education as students were older. Analysis on objectives showed that programming education uniformly affected all areas, whereas robotics education had more impact on affective domain than cognitive domain. Robot construction had the largest effect size, followed by robot construction and programming, robot programming, and robot utilization. Programming education has larger positive impacts on students overall compared to robotics education. Robotics education is more effective to upperclassmen than programming education, and improves affective domain of students. Also, robotics education shows higher efficacy when combined with various subjects.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.160-165
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• 2023

In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.181-200
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• 2020

This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.203-211
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• 2008

Due to the advent of presumer and digital production of moving picture, a lot of UCC has been generated by editing the previous moving pictures in various way. It causes a lot of problems on copyright, duplication, and content. In reorganizing the contents, it is necessary to increase productivity and reusability by managing production pipeline systematically through the standardization of moving picture content. For this purpose, we try to develop the moving picture content management system that can manage all kinds of information on the production pipeline, based on SCORM of e-learning by considering production, publication and re-editing. Using the meta-data of content object, user and producer can easily search and reuse the contents. Hence, they can choose the contents object according to their preference and reproduce their own creative UCC by reorganizing and packaging the selected objects. The management of copyright by the unit of scene would solve the problem of copyright. The sequencing technique of SCORM as an interactive storytelling method makes it possible to produce individual contents by user's preference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.992-999
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• 2014

This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

• Journal of KIISE
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• v.44 no.7
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• pp.680-685
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• 2017

The emergence of online media and their data has enabled data-driven methods to solve challenging and complex tasks such as rumor classification problems. Recently, deep learning based models have been shown as one of the fastest and the most accurate algorithms to solve such problems. These new models, however, either rely on complete data or several days-worth of data, limiting their applicability in real time. In this study, we go beyond this limit and test the possibility of super early rumor detection via recurrent neural networks (RNNs). Our model takes in social media streams as time series input, along with basic meta-information about the rumongers including the follower count and the psycholinguistic traits of rumor content itself. Based on analyzing millions of social media posts on 498 real rumors and 494 non-rumor events, our RNN-based model detected rumors with only 30 initial posts (i.e., within a few hours of rumor circulation) with remarkable F1 score of 0.74. This finding widens the scope of new possibilities for building a fast and efficient rumor detection system.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.505-517
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• 2011

The haptic sense is one of the five human senses that deeply affects cognitive development and everyday lives of children and adults. Recently, researchers and developers have started active discussions and research on haptic technologies. The purpose of this paper is to explain the role of haptics in learning, review studies that have attempted to use haptic technologies to teach students, and discuss how these technologies can be applied in special education context. National and international databases were searched and analyzed using meta-analysis methods. The few studies that have been completed so far are heavily focused on math and science learning. However, haptic technology has great potentials for children with disabilities who can benefit from extra assistance from these devices in wide areas of curriculum including math, science, music, art, history, and so on.

• Journal of the Korean Chemical Society
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• v.64 no.3
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• pp.189-195
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• 2020

In the study, the effect of flipped classroom approach applied to chemistry I class in a foreign language high school was explored. Flipped classroom was applied to 176 grade 10 students (43 boys and 133 girls) from a foreign language high school located in a metropolitan city for one semester and its instructional effects were studied in terms of cognitive and affective aspects. Before the class, students were provided with guiding worksheets and asked to summarize contents. Within the class, various student-centered activities were adopted. After the flipped classroom for one semester, mid-term and final-term exam scores were analyzed, and students' attitude toward chemistry class and flipped classroom were surveyed. Analysis on the exam scores showed the possibility for positive impact on students' achievement and perceptions on chemistry class including flipped classroom approach. Moreover, some students mentioned flipped classroom was helpful for self-directed learning and meta-cognition. Based on these results, educational implications were discussed.