• 산업융합연구
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• 제20권3호
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• pp.11-21
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• 2022

2020년 COVID-19 확산으로 인해 대학은 개강 연기, 전면적 비대면 온라인 수업 체제를 도입 및 운영하게 되었다. 이에 본 연구의 목적은 온라인 수업 시행에 따른 학습경험 분석을 통해 온라인 수업의 교수·학습의 질 제고 방안 탐색을 위한 기초자료를 제공하는데 있다. 본 연구는 한국교육개발원 2020년 대학 교수·학습과정에 관한 설문조사(NASEL) 결과 중 C대학의 자료를 활용하여, 온라인 수업 유형(실시간 수업, 녹화 수업)에 따른 학생의 학습경험과 교수·학습 성과와의 관계를 파악하고, 이 과정에서 온라인 수업에 대한 인식 및 태도의 매개효과가 발생하는지 등을 분석하고 자 구조방정식 모형을 사용하였다. 연구결과는 첫째, 학습경험이 교수·학습 성과에 정적인 영향을 미치는 것으로 나타났다. 둘째, 온라인 수업 유형에 따라 학습경험이 교수·학습 성과에 미치는 영향력이 다르게 나타났다. 녹화 수업보다 실시간 수업에서 학습경험이 교수·학습 성과에 미치는 영향력이 더 크게 나타났으며, 온라인 수업의 인식 및 태도가 교수·학습 성과에 미치는 영향력은 녹화 수업에서 더 큰 것으로 나타났다. 학습경험과 교수·학습 성과의 관계에서 온라인 수업에 대한 인식 및 태도의 매개효과는 나타나지 않았다.

• 전자통신동향분석
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• 제33권4호
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• pp.43-53
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• 2018

As face recognition (FR) has been well studied over the past decades, FR technology has been applied to many real-world applications such as surveillance and biometric systems. However, in the real-world scenarios, FR performances have been known to be significantly degraded owing to variations in face images, such as the pose, illumination, and low-resolution. Recently, visual intelligence technology has been rapidly growing owing to advances in deep learning, which has also improved the FR performance. Furthermore, the FR performance based on deep learning has been reported to surpass the performance level of human perception. In this article, we discuss deep-learning based high-performance FR technologies in terms of representative deep-learning based FR architectures and recent FR algorithms robust to face image variations (i.e., pose-robust FR, illumination-robust FR, and video FR). In addition, we investigate big face image datasets widely adopted for performance evaluations of the most recent deep-learning based FR algorithms.

• 한국정보시스템학회지:정보시스템연구
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• 제17권1호
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• pp.83-111
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• 2008

The purpose of this study is to identify which factors are important for learning performance and the moderating effects of self-regulated factors such as elaboration and organization between users characteristic(perceived usefulness, preparatory education, internet experience) and learning performance. To Accomplish these research purpose, this study performed a survey and 173 response were used for statistical analysis. The results of this study are as follows: First, 7 factors(ease of use, interactivity, accuracy, media richness, perceived usefulness, preparatory education, internet experience) had significant impacts on learning performance whereas reliability did not. Second, the moderating effects of self-regulated factors showed that Elaboration of self-regulated factors can be considered as a significant moderating variable between 2 factors(perceived usefulness, internet experience) and teaming performance.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.247-275
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• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

• 한국초등과학교육학회지:초등과학교육
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• 제29권3호
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• pp.307-315
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• 2010

In this study, we investigated the effects of cooperative learning by the levels of students' performance goal orientation in science classes on 6th graders' science achievement and science learning motivation. Two classes (47 students) from an elementary school were respectively assigned to a control group and a treatment group. A performance goal orientation test and a science learning motivation test were administered as pretests. The intervention of cooperative learning lasted for 24 class periods. A researcher-made achievement test and the science learning motivation test were administered after the instructions. ANCOVA results indicated that the score of the treatment group was significantly higher than that of the control group in the achievement test. However, no interaction was found between the cooperative learning treatment and the levels of students' performance goal orientation. There were significant aptitude-treatment interactions in science learning motivation.

• 정보과학회 논문지
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• 제44권11호
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• pp.1156-1164
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• 2017

기존의 버그 담당자 자동 배정 연구들은 대부분 기계학습 알고리즘을 기반으로 예측 시스템을 구축하는 방식이었다. 따라서, 고성능의 기계학습 모델을 적용하는 것이 담당자 자동 배정 시스템 성능의 핵심이 된다고 할 수 있으며 관련 연구에서는 높은 성능을 보이는 SVM, Naive Bayes 등의 기계학습 모델들이 주로 사용되고 있다. 본 논문에서는 기계학습 분야에서 최근 좋은 성능을 보이고 있는 딥 러닝을 버그 담당자 자동 배정에 적용하고 그 성능을 평가한다. 실험 결과, 딥 러닝 기반 Bug Triage 시스템이 활성 개발자 대상 실험에서 48%의 정확도를 달성했으며 이는 기존의 기계학습 대비 최대 69%향상된 결과이다.

• 한국정보시스템학회지:정보시스템연구
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• 제21권1호
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• pp.117-152
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• 2012

The purpose of this study was to identify the negative factors affecting personnel u-Learning acceptance and to analyze the interrelation among the factors in this research model. The two independent variables avoidable convenience and reliant convenience, based on pilot test results, and learning performance and perceived interactivity, based on the relevant literature, are used to examine the research model. The research problem was tested with data collected from 577 respondents in 23 universities. This study developed and empirically analyzed a model representing the relationship by using the Structural Equation Model. The major findings of this study are, firstly, that the higher reliant convenience is negatively affecting the degree of system use and learner’s satisfaction, whereas avoidable convenience is only affecting the learner’s satisfaction. Secondly, the higher learning performance and stronger perceived interactivity affects the degree of system use as well as learner’s satisfaction. Finally, the degree of system use affects the learner’s satisfaction.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.1-7
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• 2021

Reinforcement learning is an area of machine learning that studies how an intelligent agent takes actions in a given environment to maximize the cumulative reward. In this paper, we propose a new MAC protocol based on the Q-learning technique of reinforcement learning to improve the performance of the IEEE 802.11 wireless LAN CSMA/CA MAC protocol. Furthermore, the operation of each access point (AP) and station is proposed. The AP adjusts the value of the contention window (CW), which is the range for determining the backoff number of the station, according to the wireless traffic load. The station improves the performance by selecting an optimal backoff number with the lowest packet collision rate and the highest transmission success rate through Q-learning within the CW value transmitted from the AP. The result of the performance evaluation through computer simulations showed that the proposed scheme has a higher throughput than that of the existing CSMA/CA scheme.

• 경영과정보연구
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• 제27권
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• pp.71-99
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• 2008

This study investigates the relationships between three BSC communication attributes(support of organizational culture, message valid, and knowledge sharing) and organizational learning and how that translates into relationship organizational performance in public organization. In this paper, first, past studies on BSC communication and organizational learning that identify the attributes of effective communication and organizational learning in organizational performance are reviewed. Second, a research model, key variables, and three hypotheses tested by PLS(partial least squares) are presented. The data was collected from BSC champions and managers of 53 public organizations in Korea. The results indicate, first, BSC communication (except for support of organizational culture) have not significant related to organizational performance. Therefore, H1 was not supported. Second, the structural path coefficient between support of organizational culture and message valid and organizational learning are statistically significant and in the hypothesized direction. But the knowledge sharing has not significant relationship with organizational learning. Therefore, H2 was partially supported. Third, organizational learning was significantly positively related to organizational performance. H3 was supported. Finally, organizational learning play a significantly positive role in mediating the relationship between BSC communication and organizational performance. The theoretical contributions, limitations, as well as future research directions are discussed at the end of the paper.