• Journal of Technologic Dentistry
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• v.44 no.4
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• pp.132-143
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• 2022

Purpose: The study aims to analyze differences in the satisfaction level of dental technology students regarding in-person and online classes. It also aims to provide fundamental resources for the improvement of major subject class methods that will improve students' self-directed learning abilities, thereby affecting their class satisfaction. Methods: In this study, a self-administered questionnaire was conducted from November 8 to November 30, 2021, for 256 dental technology students. The collected data were analyzed using the IBM SPSS Statistics ver. 21.0 statistical program. Frequency and percentage, mean, standard deviation, t-test, ANOVA, post-hoc test, correlation analysis, and linear regression analysis were performed to analyze the data. Results: In the self-directed learning abilities, the attitude of the learners was shown to have the highest positive (+) correlation in both in-person and online classes, with a statistically significant effect (p<0.001) on class satisfaction in major subject classes. Moreover, the explanatory power of the model was 52.2% and 39.7%, respectively. Conclusion: We concluded from the study that there is a need for professors to improve teaching methods to increase learners' self-directed learning competence, through problem-based learning, discussion learning, team-based collaborative learning, and mentor-mentee learning, thereby enabling learners to lead classes themselves.

• Journal of East Asia Management
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• v.4 no.2
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.633-640
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• 2021

This study analyzes the effects of text and image presenting methods in video lectures on students' learning flow, learning satisfaction and learning outcomes. The text presenting methods include forming short sentences of 2 or 3 words or using key words, while image presenting methods include images featuring both detailed and related information as well as images containing only related information. 167 first year students from Xingtai University were selected as experimental participants. Groups of participants were randomly assigned to engage in four types of video. The research results are as follows. First, it was found that learning flow, learning satisfaction and learning outcomes of group presented with video forms of short sentences had higher statistical significance compared to the group experiencing the key word method. Second, learning flow, learning satisfaction and learning outcomes of group presented with video forms of only related information had higher statistical significance compared to the group experiencing the presenting method of both detailed and related information. That is, the mean values of dependent variables for groups of short form text and only related information were highest. In contrast, the mean values of dependent variables for groups of key words and both detailed and related information were the lowest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2101-2121
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• 2020

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos' repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users' interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users' interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6176-6186
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• 2014

This study was designed to clarify the learning motivations, academic self-efficacy, and problem-solving processes in the educational evaluative method in the fundamental nursing practice using moving pictures. The learning motivations and academic self-efficacy showed statistical differences based on the students' motivations of selecting, their satisfaction with the major and nursing practice, helpfulness of moving pictures, and the suitability of practical tests using a checklist. Problem-solving processes revealed statistical differences based on the students' motivations of selecting the nursing department, their satisfaction with the major and fundamental nursing their satisfaction with the major and nursing practice, the helpfulness of moving pictures, and the suitability of practical tests using a checklist. The learning motivations showed significant positive interrelations with the academic self-efficacy and problem-solving processes. In conclusion, the educational evaluative method in the fundamental nursing practice using moving pictures was related to the nursing students' learning motivations, academic self-efficacy, and problem-solving processes.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• Journal of Industrial Convergence
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• v.21 no.7
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• pp.29-39
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• 2023

In the recent period, there have been numerous earthquakes both domestically and internationally, and buildings in South Korea are particularly vulnerable to seismic design and earthquake damage. Therefore, the objective of this study is to discover an effective method for assessing the seismic vulnerability of buildings and conducting a density analysis of high-risk structures. The aim is to model this approach and validate it using data from pilot area(Seoul). To achieve this, two modeling techniques were employed, of which the predictive accuracy of the statistical analysis technique was 87%. Among the machine learning techniques, Random Forest Model exhibited the highest predictive accuracy, and the accuracy of the model on the Test Set was determined to be 97.1%. As a result of the analysis, the district rating revealed that Gwangjin-gu and Songpa-gu were relatively at higher risk, and the density analysis of at-risk buildings predicted that Seocho-gu, Gwanak-gu, and Gangseo-gu were relatively at higher risk. Finally, the result of the statistical analysis technique was predicted as more dangerous than those of the machine learning technique. However, considering that about 18.9% of the buildings in Seoul are designed to withstand the Seismic intensity of 6.5 (MMI), which is the standard for seismic-resistant design in South Korea, the result of the machine learning technique was predicted to be more accurate. The current research is limited in that it only considers buildings without taking into account factors such as population density, police stations, and fire stations. Considering these limitations in future studies would lead to more comprehensive and valuable research.

• Journal of Korean Academy of Nursing
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• v.29 no.4
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• pp.963-976
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• 1999

This study was undertaken to investigate the general characteristics of students, which include the degree of satisfaction, motives of admission, the recognition of advantages and disadvantages, opinion of students on self-directed learning, and planning and anticipatory effects after graduation. Data was collected through a questionnaire survey over a period of four months, from May 1997 to August 1997. The subjects used for this study consisted of 322 RN students sampled from six RN-to-BSN programs in Korea using the census sampling method. Statistical methods employed for this study included discriptive statistics, M ANOVA, and F-test. The results of the study are as follows 1. The RN students' motives of admission to RN-to-BSN programs were ‘for personal advancement’, ‘to earn a BSN degree’, and ‘for professional development’ in this order. 2. The RN students' responses to the advantages of RN-to-BSN programs were ‘acquisition of new knowledge and a BSN degree’ and ‘to gain professional thinking and a broader view’, while as the disadvantages of RN-to-BSN programs were ‘geographical isolation of institutions’, ‘limitation of information’, and ‘underdeveloped school environments’ in this order. 3. The survey based on opinions toward self-directed learning showed that there was a need of detailed guidelines for self-directed learning. Most agreed that it was a very effective learning method for a RN student, and the self-directed learning method Increases motives for learning. 4. The students' anticipatory effect after graduation were ‘self-achievement’, ‘development of professional skills’, and ‘admission to post-graduate school or programs to study abroad’. 5. The students were very satisfied with the quality of faculty members, and satisfied with the quality of lectures and teaching. However, students were unsatisfied with rented lecture rooms, and very unsatisfied with self-directed learning methods. 6. School nurses showed higher statistical significances in the need for teaching material and anticipatory effect after graduation than other RN students working in hospitals and public health agencies. Also, school nurses, public health nurses, and industry nurses showed higher statistical significances in motives of admission than RN students working in hospitals. Further more, staff nurses, school nurses, and industry nurses showed higher levels of satisfaction toward a RN-to-BSN programs than nurses in higher positions, such as administrators or directors of nursing. 7 City residents were more satisfied with RN-to-BSN programs than rural residents. Otherwise, the rural residents had higher motives for admission, a bigger need for teaching materials, and recognition of the disadvantages of RN-to-BSN programs than city residents. Finally, RN students who earned below a monthly income of ￦1,000,000 showed higher motivation for admission than those who earned more than ￦1,000,000.

• Korean Journal of Adult Nursing
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• v.17 no.4
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• pp.635-645
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• 2005

Purpose: This study was attempted and completed in order to settle down and qualitatively improve web-based nursing education by evaluating effect and managing strategy of tutor. Method: The development of tutor's managing strategy was based on "The Self-regulated Learning" and "The supportive Learning", then it was applied to 79 learners in one of the cyber-learning centers. After applying the tutor's managing strategy, self-regulated learning scale, attitude for school, preference for computer and academic achievement were evaluated. The development of tutor's managing strategy for web-based nursing education are consisted of participation promotion, psychological support and motivation, recognition and promotion strategy of learning activity, management strategy of evaluating stage. Result: The levels of learner's self-regulated learning, recognition, behavior, attitude on the school and learning achievement were meaningfully increased in statistics after applying for the managing strategy of tutor. The motivation level and learning participation kept high scores from the beginning with no significant statistical changes. Conclusion: It is required to develop an educational program for cultivating well-educated tutors in order to help the effective learning process of nurses based on understanding characteristics of learners.