• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.1
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• pp.30-41
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• 2011

This study performs the subjective evaluations of LED illumination conditions according to three types of color temperatures (7,000[K], 5,000[K], 3,000[K]) after employing LED illumination system in the classroom, Since the objective of this study is to develop an artificial lighting conditions like day light comfortable to students in the classroom, the learning effect based on three types of LED illumination conditions are analyzed. Three factors (learning intention, learning environment, learning motivation) are extracted by ANOVA and there are preference differences of LED illumination conditions between learning intention and learning environment factors. Especially, preference differences of LED illumination conditions are existed about calculation, reading and fatigue reduction. The test results of this study can be applied to obtaining high achievement of learning based on the lighting conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.619-624
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• 2014

This paper introduces a robot-based learning system which is designed to teach multiplication to children. In addition to a small humanoid and a smart device delivering educational content, we employ a type of mixed-initiative operation which provides enhanced multi-modal cognition to the r-learning system through human intervention. To investigate major factors that influence people's intention to use the r-learning system and to see how the multi-modality affects the connections, we performed a user study based on TAM (Technology Acceptance Model). The results support the fact that the quality of the system and the natural interaction are key factors for the r-learning system to be used, and they also reveal very interesting implications related to the human behaviors.

• Educational Technology International
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• v.21 no.1
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• pp.97-123
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• 2020

The purpose of this study is to investigate the emotions of students toward using digital textbooks, and to examine the factors affecting the emotions. This study examined the relationship between individual characteristics and computer usage, students' emotions, and the perceived learning effects. For this study, 2,950 1st grade middle school students participated in a survey which measured individual characteristics, computer usage behavior, emotions toward using digital textbooks, and perceived learning effects of digital textbooks. The results showed that positive emotions toward using digital textbooks were higher than negative emotions. The students' positive emotions were most affected by intrinsic motivation, self-regulated learning, and student's use of computers for learning and entertainment. Similarly, perceived learning effects were positively correlated to intrinsic motivation and self-regulated learning, but the students' positive emotions towards using digital textbooks was the strongest predictor. Digital textbook efficacy was the most influential factor that affected the students' negative emotions, while computer addiction was associated with negative emotions.

• The Journal of Information Systems
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• v.32 no.4
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• pp.189-210
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• 2023

Purpose This study aims to investigate factors that positively influence the continuous Artificial Intelligence(AI) Learning Continuity of business major students. Design/methodology/approach To evaluate the impact of AI education, a survey was conducted among 119 business-related majors who completed a software/AI course. Frequency analysis was employed to examine the general characteristics of the sample. Furthermore, factor analysis using Varimax rotation was conducted to validate the derived variables from the survey items, and Cronbach's α coefficient was used to measure the reliability of the variables. Findings Positive correlations were observed between business major students' AI Learning Continuity and their AI Interest, AI Awareness, and Data Analysis Capability related to their majors. Additionally, the study identified that AI Project Awareness and AI Literacy Capability play pivotal roles as mediators in fostering AI Learning Continuity. Students who acquired problem-solving skills and related technologies through AI Projects Awareness showed increased motivation for AI Learning Continuity. Lastly, AI Self-Efficacy significantly influences students' AI Learning Continuity.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.3
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• pp.1-15
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• 2022

Purpose - This study explored factors of online education that affect student dissatisfaction and learning effectiveness in higher education during the COVID-19 pandemic. Research design, data, and methodology - This study combined qualitative and quantitative designs. The qualitative part of this study involved in-depth interviews using a criteria-based purposive sampling technique. The quantitative part of this study consisted of an online survey. Results - The qualitative results revealed that students faced significant problems related to online learning, including a lack of learning environment, interaction, and support from the school. The quantitative results indicated that the effects of transitioned-online courses on student dissatisfaction were higher with student support, the interaction between students and instructors, online learning environment, and course organization and evaluation based on the order, while the effects on learning effectiveness were higher with the online learning environment, interaction between students and instructors, course organization and evaluation, and student support based on the order. Conclusion - The results implied that online learning in the era of the COVID 19 pandemic negatively affects student satisfaction and learning effectiveness. Policymakers and school leaders should improve students' satisfaction and learning effectiveness when confronted with the pandemic. Better policies should be adopted to improve better way of teaching in the era of COVID19.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.1
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• pp.118-132
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• 2023

This study aimed to analyze the relationship between the learning perception of aviation service subjects using role play on the interpersonal competence, learning satisfaction, and learning outcomes of college students majoring in aviation service. A survey was conducted targeting 217 university students majoring in aviation service who are enrolled in universities in the metropolitan area and Chungcheong area and have experience in class using role play. In addition, the hypothesis is verified using the structural equation model. Among the perceptions of role play classes experienced by university students majoring in aviation service, 'inter-dependency' was found to have a positive (+) effect on all sub-factors of interpersonal competence. And 'usefulness' confirmed the positive (+) influence relationship with 'temperament for others' and 'communication' except for 'relationship promotion' of interpersonal ability. Among sub-factor of interpersonal competence, it is confirmed that only the factors of 'temperament for others' and 'communication' had an effect on learning satisfaction. And it is proved that learning satisfaction is an important determinant of learning outcomes. Therefore, this study is expected to serve as a basis for deriving an efficient and optimal learning method for practical subjects in aviation service majors.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.3
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• pp.251-260
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• 2021

Purpose: This study uses a descriptive research design to identify the influence of critical thinking disposition, deep approaches to learning, and interaction between learners on the degree of nursing process confidence for nursing students. Methods: The subjects of the study were second-year students in the Department of Nursing at a university in G city. The data included general characteristics, critical thinking disposition, deep approaches to learning, learner-to-learner interaction, and nursing process confidence were analyzed utilizing an independent t-test, one-way ANOVA, and Scheffe's test to identify differences in the variables according to general characteristics. To identify the correlation between the factors related to the nursing process and nursing process confidence, Pearson's correlation was analyzed, and hierarchical regression was used to determine the factors affecting the confidence of the subject's nursing process. Results: Gender, critical thinking disposition, and in-depth learning approach were statistically significant as factors affecting the nursing process confidence of nursing students, and these factors were shown to explain 62% of nursing course performance (F=23.80, p<.001), among which in-depth learning access has the greatest influence (β=.41, p<.001). Conclusion: Critical thinking disposition and deep approaches to learning arbitration program development are necessary to improve nursing students' nursing process confidence.

• Journal of Elementary Mathematics Education in Korea
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• v.16 no.1
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• pp.39-61
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• 2012

Mathematical creativity is essential in school mathematics and mathematics curriculum and ensures the growth of mathematical ability. Therefore mathematics educators try to develop students' creativity via mathematics education for a long time. In special, 2011 revised mathematics curriculum emphasizes mathematical creativity. Yet, it may seem like a vague characterization of mathematical creativity. Furthermore, it is needed to develop the methods for developing the mathematical creativity. So, the goal of this paper is to search for teaching and learning models for developing the mathematical creativity. For this, I discuss about issues of mathematical creativity and extract the factors of mathematical creativity. The factors of mathematical creativity are divided into cognitive factors, affective factors and attitude factors that become the factors of development of mathematical creativity in the mathematical instruction. And I develop 8-teaching and learning models for development of mathematical creativity based on the characters of mathematics and the most recent theories of mathematics education. These models make it crucial for students to develop the mathematical creativity and create the new mathematics in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.63-83
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• 2021

The appearance of education is also rapidly changing in social changes represented by social networks. And the development of information and communication technology is also having a widespread effect on the education field. In the era of untact caused by Covid-19, education through smart learning is having a greater effect on students as well as adult learners more quickly and broadly. In addition, smart learning is not just limited to learning content, but is developing into personalized, convergence, and intelligent. The purpose of this study is to identify the factors of ARCS motivation theory that can determine the learning motivation of smart learning users, and to empirically study the casual relationship between these factors on education achievement through practical value and hedonic value. Specifically, I would like to examine how the independent variables ARCS motivation factors (attention, relevance, confidence, and satisfaction) affect learners' education achievement through the parameters of practical value and hedonic value. To this end, a research model was presented that applied the main variables of attention, relevance, confidence, and satisfaction, which are four elements of ARCS motivation theory, a specific and systematic motivational strategy to induce and maintain learners' motivation. In order to empirically verify the research model of this study, a survey was carried out on learners with experience using smart learning. As a result of the study, first attention was found to have a positive effect on the hedonic value. Second, relevance was found to have a positive effect on the hedonic value. Third, it was found that confidence did not have a positive effect on the practical value and the hedonic value. Forth, satisfaction was found to have a positive effect on the practical value and the hedonic value. Fifth, practical value was found to have a positive effect on the education achievement. Sixth, hedonic value was found to have a positive effect on the education achievement. Through this, it can be seen that the intrinsic motivation of learners using smart learning affects the education achievement of users through intrinsic and extrinsic value. A variety of smart learning that combines advanced IT technologies such as AI and big data can contribute to improving learners' education achievement more effectively and efficiently. Furthermore, it can contribute a lot to social development.