Policy and rule configuration

행사명 : Workshop in Political Theory and Policy Analysi

Development of Hybrid Teaching Method Using MOOCs

Abstract: The purpose of this study is to explore a new teaching and learning method using MOOC as a way of solving the change of the educational environment and the learner 's unsatisfied academic desire. This study suggests 'Hybrid teaching method' as a way to utilize MOOC in liberal arts education. This teaching method is a method of perfect learning through formal learning and informal learning. Formal learning consisted of a teacher -centered knowledge acquisition stage and a learner -centered thinking extension stage. Informal learning consists of learner -led experience/reflection stages. It is a characteristic of 'Hybrid teaching method' that emphasizes the role of instructor, breaks the boundaries of education space through informal learning, and implements perfect learning through level learning. This study is meaningful that the MOOC has provided a detailed plan to utilize it in the curriculum of the university. We hope that the 'Hybrid teaching method' using MOOCs will be a way to adapt and lead the liberal education to the future education trends as a customized learning according to the development of science and technology, destruction of spatial areas such as online and offline, emphasis on connection between theory and reality through formal learning and informal learning

Application of Hybrid Teaching Method Using the MOOC and Verification of its Effectiveness

Purpose Given significant changes in educational environments characterised by the destruction of academic boundaries and the advent of digital natives. We aimed to verify the effectiveness of a hybrid teaching method in improving contemporary learning environments. Methodology A descriptive comparative study involving two groups of 68 students: a half used traditional teaching methods and the other half involved in hybrid teaching methods. To verify the effectiveness of the hybrid teaching method in improving the teaching quality. The participants from both groups were asked to answer surveys inquiring about their experiences with the learning processes, their achievements and degree of understanding, perceptions on field experience value, and learner satisfaction. Results Reports on hybrid teaching methods showed significant differences in learning and understanding. Field experiences had substantial effects on learning performance and degrees of knowledge acquisition, serving to answer questions of relevance. Students had a higher preference for online over offline courses; they could take classes without time and space constraints. The hybrid methods group were highly satisfied while those in traditional classes had difficulties understanding and questioning concepts. Instructors put excessive effort into digital literacy education and adaptation to smart learning rooms used. Conclusion The success of plans to utilise and incorporate MOOCs into learning events, along with introduction of new teaching methods was significant. Education trends will be characterised by customised learning aligned with developments in science and technology, destruction of spatial boundaries between on- and offline activities and emphasis on connections between theory and reality. Digital literacy for both professors and students is critical, as are reviews of educators’ roles, and integration of class-room and self-directed learning

Developing a Learning Data Collection Platform for Learning Analytics in Online Education

During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them

Developing a Learning Data Collection Platform for Learning Analytics in Online Education

During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them

Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining

In 2016, the number of mobile phone subscriptions worldwide had surpassed the total world population; moreover, the number of smartphone addicts is increasing each year. Thus, the objective of this study is to analyze smartphone addiction by considering the differences between smartphone usage patterns as well as cognition. Our proposed method involves automatically collecting and analyzing data through an app instead of using the existing self-reporting method, thereby improving the accuracy of data and ensuring data reliability from respondents. Based on the results of our study, we observed that there is a significant cognitive bias between the self-reports and automatically collected data. As a result of applying data mining, among the six criteria out of the total 24 items of the questionnaire, the higher the “recurrence” item, the higher the addiction; further, “forbidden” item 1 had the largest effect on addiction. In addition, the input variables that have the greatest influence on the high-risk users were the number of times the screen was turned on and real-use time/cognitive-use time. However, the amount of data and time of smartphone usage were not related to addiction. In the future, we will modify the app to obtain more accurate data, based on which, we can analyze the effects of smartphone addiction, such as depression, anxiety, stress, self-esteem, and emotional regulation, among others

Pif: In-flash acceleration for data-intensive applications

© 2022 ACM.To minimize unnecessary data movements from storage to a host, processing-in-storage (PiS) techniques, which move a compute unit to storage, have been proposed. In this position paper, we propose an extreme version of PiS solutions, called a processing-in-flash (PiF) scheme, that moves computation inside flash chips where data are physically present. As a key building block of a PiF solution, we present a novel flash chip architecture, CoX. Using a prototype PiF SSD based on CoX chips, we demonstrate that PiF-based SSDs are promising in accelerating data-intensive applications.N

Improving I/O Performance of Large-Page Flash Storage Systems Using Subpage-Parallel Reads

Handling small read requests is important on large-page flash storage systems because small reads tend to waste the read bandwidth. We present a system level solution that avoids wasting the read bandwidth based on a new page read operation, called the subpage-parallel read (SPREAD). SPREAD achieves an optimal latency for a small read request by reading requested subpages only. By improving the read performance of applications as well as garbage collection, SPREAD improves the overall I/O performance. Experimental results show that an SPREAD-aware FTL can improve the IOPS and read latency by up to 122% and 56%, respectively.N