• Educational Technology International
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• 제15권2호
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• 한국간호교육학회지
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• 제16권1호
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• pp.24-32
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• 2010

Purpose: This study was conducted to evaluate the results after implementing a simulation based critical care nursing education with $MicroSim^{(R)}$. Method: Simulation based education was used for a clinical scenario on a patient with chronic obstructive pulmonary disease(COPD) and acute coronary syndrome(ACS). Self-learning program was used for an acute asthma attack and acute myocardial infarction(AMI) in the $MicroSim^{(R)}$. A total of 97 nursing students were chosen. A pretest and posttest was conducted to evaluate learning achievement, clinical performance ability and self-directed learning. Result: Learning achievement and clinical performance ability significantly increased but self-directed learning did not. Conclusion: Simulation based education used with $MicroSim^{(R)}$ was useful for improving learning achievement and clinical performance ability of nursing students. Further studies are needed to compare the effects of simulation based education.

• 한국IT서비스학회지
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• 제15권4호
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• 대한간호학회지
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• 제49권2호
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• pp.171-180
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• 2019

Purpose: The Korean Triage and Acuity Scale (KTAS) is a tool used to classify the severity and urgency of emergency department (ED) patients, focusing on their symptoms. In consideration of the importance of the KTAS, a web-based learning program has emerged as a new mode of education; it enables ED triage nurses to access it anytime and anywhere, and according to their own learning abilities. This study aimed to develop a web-based KTAS learning program and evaluate its effects on self-efficacy and triage performance ability in ED nurses. Methods: A quasi-experimental design with a non-equivalent control group pretest-posttest was used. The conceptual framework was Bandura's self-efficacy theory. There were 30 participants in the experimental group and 29 in the control group. The experimental group attended an orientation and 4 sessions of a web-based KTAS learning program. The learning program lasted 280 minutes over five weeks, consisting of 40 minutes of orientation and four 60-minute sessions. Results: The scores of self-efficacy, triage performance ability in KTAS level, and chief complaints significantly increased in the experimental group compared to the control group. In addition, the numbers of under-triage in KTAS significantly decreased in the experimental group in comparison to the control group. Conclusion: The results suggest that the learning program was effective in improving ED nurses' level of self-efficacy and triage performance ability (KTAS level and KTAS chief complaint). Accordingly, the web-based KTAS learning program can be applied as an education intervention to improve ED nurses' triage skill.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• 경영과학
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• 제26권3호
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• pp.37-54
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• 2009

E-learning is a networked phenomenon allowing for instant revisions and distribution, and goes beyond training and instruction to the delivery of information and tools to improve performance. The proponents of e-learning emphasizes that students learn more effectively when they interact and are involved with other students participating in similar endeavors. The paper outlines the process of development and design of e-learning based Management Sciences course, with the aim of ensuring widespread use, in undergraduate business program. Experiences in introducing students to e-learning course are reported. Feedback from students has been very positive but also indicates the need for ongoing support and direction. In addition, a survey was used to identify the determinants of students' academic performance of Management Science, and PLS based model is developed to analyze the results. Statistical results concerning the hypothesized model are provided.

• 대한안전경영과학회지
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• 제12권4호
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• pp.289-300
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• 2010

The purpose of this study is to examine the effect of psychological capital on empowerment and learning performance and the mediating effect of the empowerment on the relationship between psychological capital and learning performance. In order to verify the relationships and mediating effect, data were obtained from 283 university students in Ulsan Metropolitan City and were analyzed by using SPSS 12.0, AMOS 5.0. The findings are as follows: First, the psychological capital were positively related to the empowerment and the learning performance. Second, there was also a positive relationship between the empowerment and the learning performance. Finally, it is found that empowerment fully mediated the relationship of psychological capital and earning performance. The theoretical implication of the study includes that this study and findings advance the understanding of learning performance by suggesting a new viewpoint regarding how psychological capital and empowerment to motivate university's learning performance. Based on these findings, the implications and the limitations of the study were presented including some directions for future studies.

• 전기전자학회논문지
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• 제22권4호
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• 한국컴퓨터정보학회논문지
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• 제29권4호
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• pp.9-21
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• 2024

최근 딥러닝 분야가 빠르게 발전하는 가운데, 다양한 영역에서 거대 언어 모델을 활용하기 위한 많은 연구들이 진행되고 있다. 하지만 언어 모델의 개발 및 활용을 위해서는 방대한 데이터와 고성능 자원이 필요하다는 현실적인 어려움이 존재한다. 이에 따라 프롬프트를 활용하여 언어 모델을 효율적으로 학습할 수 있는 문맥 내 학습이 등장하였지만, 학습에 효과적인 프롬프트가 무엇인지에 대한 명확한 기준은 구체적으로 제시되지 않았다. 이에 본 연구에서는 문맥 내 학습 방법 중 하나인 PET 기법을 활용하여 기존 데이터의 문맥과 유사한 PVP를 선정하고, 이를 통해 생성한 프롬프트를 학습하여 모델의 성능을 향상시킬 수 있는 프롬프트 기반 학습 성능 향상 방법론을 제안한다. 제안 방법론의 성능 평가를 위해 온라인 비즈니스 리뷰 플랫폼인 Yelp에서 수집된 레스토랑 리뷰 데이터 30,100개로 실험을 수행한 결과, 제안 방법론이 기존의 PET 방법론에 비해 정확도와 안정성, 그리고 학습 효율성의 모든 측면에서 우수한 성능을 보임을 확인하였다.