• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.156-166
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• 2021

The purpose of this study is to find out the impact of classes using a game-based student response system on learning engagement. It is an experimental study that compares learning engagement in classes (experimental groups) and lecture-style classes (comparative groups) that utilize GSRS in nursing education. A total of 211 nursing students participated from October 2019 to December 2019. The differences in learning engagement between the two groups were analyzed as t-test and correlation analysis was conducted on related factors. There was a difference between the comparison group and the experimental group in overall learning engagement(p=.013) and emotional engagement(p=.002). This is meaningful in that it has verified the learning engagement effect of the GSRS for the first time in Korea.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.563-585
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• 2020

This study developed teaching-learning materials for mathematics and coding convergence classes using Python, focusing on 'Prime Factorization' of seventh graders. After applying the teaching methods and contents to the students, they analyzed whether the learners achieved their learning goals. The results were used to modify and supplement teaching and learning materials. Affective domain of learners were also analyzed. The results are that the teaching methods and contents of the developed teaching-learning materials were generally appropriate for learners. The learners understood most of the lessons according to the set teaching methods of all classes. And learners have mostly reached their learning goals. In addition, as a result of analyzing the definition characteristics of learners through follow-up interviews, the interest in mathematics and programming has improved. The developed teaching and learning materials of this study are well consisted mostly of the teaching methods and the contents of the classes, and are organized so that learners can reach most of the learning goals. It also brought positive changes to the affective domain of mathematics and coding, demonstrating the potential for useful use in school.

• Communications of Mathematical Education
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• v.27 no.4
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• pp.473-486
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• 2013

This study examines the learning styles of freshmen enrolled in a college of science and engineering, and analyses the relationship between learning styles and academic achievement in mathematics to provide basic data for the teaching-learning methods, which are more suitable to learning styles of students. For the purpose of this research, a reliability analysis of Kolb's LSI is applied to 282 freshmen enrolled in a college of science and engineering of the medium-sized university. The outcomes of this survey are followings. Firstly, students hold higher positions in the order of converger, assimilator, accommodator, diverger among 4 learning styles. Secondly, while there is a positive corelation between abstract conceptualization[AC] and academic achievement, there is a negative corelation between concrete experience[CE] and academic achievement. Thirdly, as for academic achievement in mathematics, converger is superior to assimilator and accommodator. Finally, the correlation between learning styles and academic achievement is different by demographic characteristics. Based on these results, this study suggests the necessity for various teaching-learning strategies, which are adjusted to both academic characteristics of mathematics and learning styles. Also, the need for teaching methods, which help students to develop effectively four learning cycles, is proposed.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• Communications of Mathematical Education
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• v.30 no.1
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• pp.85-99
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• 2016

The research was carried out to be part of a mathematics app stimulation that enables the elderly to learn mathematics by using Smart devices. Particularly appropriate method / function that leads to learning is very important for people who are not accustomed to Smart devices like the elderly. The research was conducted to build affordance strategy based on the consideration of characteristics of senior learners. It aims to achieve both the goals of education through mathematics learning materials provided by smart devices and also to improve user convenience. It suggests cognitive, physical and sensory features and factors to improve affordance of Smart learning system.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.838-848
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• 2021

Parents' influence on children's development is generally considered essential. This paper attempts to explore the role of AR in preschool education from the perspective of parents, aiming to help parents better understand the impact of children's use of augmented reality in preschool education. The subjects were parents of children in the preschool age range (3-6 years old), and the experimental equipment was AR cognitive cards. In order to extract parents' views on AR, five parents were invited to conduct an experiment with their children using AR cognition cards, and then an open interview survey was conducted. In the second experiment, the answers obtained from the first experiment were sorted out and formed a questionnaire to conduct a closed-book survey. It shows that parents are satisfied with the characteristics of AR to assist their children's learning. At the same time, parents also value technology, usage management and playing environment. AR can stimulate children's learning initiative. Children like to use AR, AR is suitable for learning, make parents satisfied. But even if AR is suitable for learning, parents will control the time their children use it.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• The Journal of Information Systems
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• v.32 no.4
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• pp.229-245
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• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.

• School Mathematics
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• v.17 no.2
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• pp.203-222
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• 2015

This study analyzed the classroom discourse between teacher and students based on the Mehan(1979a)'s theory to examine the characteristics of the classroom discourse between teacher and students in high school statistics class. The results of this study on the structure of class showed that the statistics class in this study adopted knowledge transmission-oriented teacher-led class in which the framework of introductiondevelopment- arrangement, which is Mehan's basic 3 stages, is clearly represented. The results of examining I-R-E sequence showed that $I_T-R_T$ structure, in which the teacher asks questions and the teacher talks about the answer, frequently appeared. And the statistics class in this study was monological class in which students hardly participated. Through these results of this study, it was found that teacher should form the statistical context, in which students can participate in discourse, and build discourse learning community and induce argumentational discourse through metaprocess elicitation.

• 한국HCI학회:학술대회논문집
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• 2007.02b
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• pp.206-212
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• 2007

웹을 기반으로 하는 e-러닝에 대한 교육적 수요는 증가하고 있다. 이와 동시에, 학습 공간으로서의 사이버 공간의 활용에 대한 고민도 증가하였다. 전통적인 학습활동을 사이버 공간에 복제하려 했던 고전적 방식이 e-러닝 또는 사이버 학습이 아니라는 사실을 확인하기 시작했기 때문이다. e-러닝의 가치가 강조됨에도 불구하고, 실제 사이버 공간에서 일어나는 학습자의 특성과 학습활동이 구체적으로 어떻게 일어나는 지에 대한 탐색은 미흡하다. 산재한 정보를 스스로 가공한 지식, '학습하는 방법을 학습'하는 것이라는 개념들이 제시됨에도 불구하고, 사이버 공간에 산재한 정보, 학습하는 방법의 학습, 그리고 사이버 공간의 학습특성에 대한 논란은 여전하다. 본 연구에서는 실제 사이버 학습 사이트를 이용하고 있는 학습자들의 행동을 중심으로, 학습자의 특성을 탐색하였다. 사이버 공간에서 보이는 스스로 학습하는 방법이 무엇인지 확인하고 이것이 다양한 학습자 유형으로 구분되는 지를 확인하고자 하였다. 연구대상이 된 사이버 학습 사이트는 서울, 부산, 대구, 광주 교육청에서 운영하는 사이버 가정 학습관이었다. 총 1535명의 사이버 가정 학습관 이용자들의 특성이 분석되었다. 사이버 가정학습관 이용자들의 행동특성은 9개의 요인-놀이 활동, 공동 경험, 현실 정체, 공동 성취, 개인주의, 경쟁 지향, 성취감, 편리성(조작 용이), 생생함-으로 구분되었다. 9개의 활동 요인을 기준으로 하여 확인된 학습자 유형은 4가지로 나타났다. 4가지 학습자 유형은 각각 독야청청형, 동고동락형, 의무방어형, 희희낙낙형으로 명명되었다. 이들 유형은 학습 활동 정도 및 사이트 이용 행동, 학습 스타일(사이버 학습 활동 양식)에서 서로 차이가 있었다. 본 연구는 기존의 이론적인 모델에 기초하여 임의적으로 구분된 사이버 학습자 유형 구분이 아닌, 실제 학습 활동을 탐색하였다는 측면에서 의미가 있다. 특히, 기존의 오프라인 학습 이론 및 학습자 특성 연구를 사이버 학습에 그대로 적용할 것이 아니라 사이버 공간의 특성이 실제 학습 활동에서 어떻게 나타났는지를 밝히려고 했다는데 그 의의가 있다. 향후, 사이버 학습자 유형에 따른, 사이버 학습활동의 촉진방안이나 학습 효과의 차이를 높일 수 있는 구체적인 학습 시스템의 설계 및 운영 모델에 대한 탐색이 필요할 것이다.