• 대한간호학회지
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• 제41권1호
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• pp.36-46
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• 2011

practice in operating room nursing and to examine the learning effects. Methods: Based on content and need analysis, 9 learning modules were developed for nursing care in operating rooms and with operating equipment. To verify the effects of the program, a quasi- experimental pretest-posttest control group design was employed. The participants in this study were 74 third-year nursing students (34 in the experimental and 40 in the control group) from a junior college in G-city, Korea, who were engaged in a one week clinical practicum in an operating unit. Frequencies, $X^2$-test and t-test with the SPSS program 17.0 were used to analyze the data. Results: Knowledge was significantly higher in the experimental group compared to the control group (p=.018). However, there was no significant difference between the two groups in self-directed learning. The experimental group had significantly higher motivation toward learning, which was examined posttest only (p=.027). Conclusion: These results indicate that the implementation of an e-Learning program needs to be continued as an effective educational tool, but more research on the best way to implement e-Learning in students' practicum is needed.

• 청정기술
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• 제28권2호
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Geomechanics and Engineering
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• 제37권1호
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.54-65
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• 2021

The movement control order and shutting down educational institution in Saudi Arabia has jeopardized the teaching and learning process. Education was shifted to distance learning in order to avoid any academic loss. In the middle of the Covid-19 crisis, there is a need to assess the full image of e-learning in Saudi Arabia. To investigate student and teachers' perception and acceptance, parents' attitudes and believes about distance education are the main goals of the study. The mix-method research design was employed to collect data. Three surveys were distributed to 100 students and 50 teachers and 50 parents from different educational institutions in Saudi Arabia, while semi-structured interviews were conducted with 10 parents. Random stratified and convenient sampling methods were adopted. Both descriptive and content analysis was conducted using SPSS25.0 and NVIVO software for quantitative and qualitative data accordingly. The findings showed that students are comfortable with remote education and are receiving enough support from schools and instructors but they think online education can't replace conventional face-to-face learning. Moreover, the results showed that teachers are having challenges in preparing online classes because of the development of conducting online classes and the lack of training. However, parents showed negative attitudes regarding the benefits and values of remote education and preferred conventional learning styles in elementary schools. Parents tended to reject and resist distance learning for several reasons: professional knowledge and lack of time to support their young kids in online classes, the shortcomings of e-learning, young children's inadequate self-regulation. Saudi parents are neither trained nor ready to use e-learning. The study provided suggestion and implications for teacher education and policymakers.

• 정보교육학회논문지
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• 제16권4호
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• pp.439-450
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• 2012

본 연구는 정보통신기술의 발달에 따른 초등교사에 대한 사회적 요구를 반영하고 대학 강의의 질을 개선하기 위하여 컴퓨터 교과교육에서 웹 2.0 기반의 문제중심학습을 통합 적용하고 그 효과를 확인하고자 하였다. 학습자들은 4회에 걸쳐 문제중심학습을 수행하였다. 그 학습 과정은 클라우드 환경의 구글 오피스를 이용하여 작성하였으며, PBL 과정 및 결과에 관한 경험을 반성 저널에 작성하였다. 학습자들의 문제 해결 과정과 반성저널은 질적 분석 방법을 통하여 분석하였으며 그 결과는 다음과 같다. 웹 2.0 기반의 문제중심학습은 컴퓨터교과교육에 대한 이해의 증진, ICT 활용 능력의 향상, 웹 2.0 기반의 교수 학습 전략의 습득, 그리고 수행평가로서 e-포트폴리오를 경험하는 등 21세기 교사에게 필요한 역량을 향상시킬 수 있었다.

• 기본간호학회지
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• 제11권1호
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• pp.6-12
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• 2004

Purpose: The purpose of this study was to evaluate self-study learning achievement in undergraduate nursing students who used a web-based wound care e-book. Method: The web-based wound care e-book was applied to 80 nursing students at Y university. The students studied the wound care e-book for four weeks and practiced wound dressing by themselves in open laboratory. Learning achievement was evaluated according to achievement of unit objectives and performance of an actual wound dressing. Result: 1. The total mean score for achievement of unit objectives was 3.06 (${\pm}0.41$) and the total mean score on the performance of the wound dressing was 89.40 (${\pm}5.47$). 2. There was no difference between the scores in the performance test (F=1.012, p=.366) for students who used self-study and those who were given a lecture. 3. A positive correlation was found between achievement of unit objectives and performance of the wound dressing (r=0.306, p<0.05). Conclusion: The web-based wound care e-book was effective in facilitating self-study for nursing students, and there is a need to continuously develop and up-date web-based nursing education e-books to facilitate self-study.

• Educational Technology International
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• 제23권2호
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• pp.207-237
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• 2022

As technological changes continue to accelerate every day, meeting the needs of a shifting educational landscape requires leaving an exclusively "in-person" education behind. Gamified learning environments should be carefully designed in light of conflicting studies to suit students' needs. The purpose of this meta-analysis is to draw conclusive results regarding the application of the most commonly used game elements in education, i.e., badges and leaderboards, through a comprehensive analysis of their impact on academic performance in online learning. Review Manager (RevMan 5.4) was used to analyze eligible studies selected from Emerald, SAGE, ERIC, EBSCO, and ProQuest between January 2011 and January 2022. Analyzing 37 studies found that using leaderboards and badges in online education enhanced academic performance when compared to traditional learning without gamification (SMD = 0.39). The badge-only intervention showed a larger effect size (SMD = 0.33) than the leaderboard-only intervention (SMD = 0.27). Badges and leaderboards together exhibited a larger effect size (SMD = 0.48) than individual game elements (SMD = 0.40). The impact of the game elements on academic performance was greater in the humanities (SMD = 0.51) than in STEM fields (SMD = 0.32) and was greater for K-12 students (SMD = 0.63) than for college students (SMD = 0.31). This study contributes to a timely discussion of the use of badges and leaderboards in COVID-19 online learning trends and provides relevant data for designing integrations of online education and gamification models.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• 한국멀티미디어학회논문지
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• 제12권5호
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• pp.757-766
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• 2009

본 연구에서는 학습자 행동모델을 이용하여 개별적인 학습 환경을 제공할 수 있는 적응적 하이퍼미디어 학습 시스템을 제안하였다. 본 시스템에서는 학습자의 학습행동정보를 실시간으로 추적하여 관리할 수 있는 LBML을 제안하였다. 제안 시스템은 학습행동정보 수집시스댐과 적용적 학습지원시스템으로 구성된다. 학습행동정보 수집시스템은 웹 2.0기술을 이용하여 SCORM CMI 데이타 모델을 기반으로 학습자의 학습행동정보를 실시간으로 수집한다. 수집된 학습행동정보는 LBML 스키마를 기반으로 개별 학습자의 LBML 인스턴스로 저장된다. 적웅적 학습지원시스댐에서는 LBML 인스턴스를 분석하여 학습자의 반웅에 대한 즉각적인 피드백을 제공할 수 있는 규칙기반 학습지원모률과 상호작용적 학습지원모듈을 개발하였다.

• Korean Journal of Mathematics
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• 제32권1호
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• pp.43-57
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• 2024

Given a domain X and a collection H of functions h : X → {0, 1}, the Vapnik-Chervonenkis (VC) dimension of H measures its complexity in an appropriate sense. In particular, the fundamental theorem of statistical learning says that a hypothesis class with finite VC-dimension is PAC learnable. Recent work by Fitzpatrick, Wyman, the fourth and seventh named authors studied the VC-dimension of a natural family of functions ℋ'²_t(E) : 𝔽²_q → {0, 1}, corresponding to indicator functions of circles centered at points in a subset E ⊆ 𝔽²_q. They showed that when |E| is large enough, the VC-dimension of ℋ'²_t(E) is the same as in the case that E = 𝔽²_q. We study a related hypothesis class, ℋ^d_t(E), corresponding to intersections of spheres in 𝔽^d_q, and ask how large E ⊆ 𝔽^d_q needs to be to ensure the maximum possible VC-dimension. We resolve this problem in all dimensions, proving that whenever |E| ≥ C_dq^d-1/(d-1) for d ≥ 3, the VC-dimension of ℋ^d_t(E) is as large as possible. We get a slightly stronger result if d = 3: this result holds as long as |E| ≥ C_3q^7/3. Furthermore, when d = 2 the result holds when |E| ≥ C_2q^7/4.