7 results
Search Results
2. Research Landscape of Smart Education: A Bibliometric Analysis
- Author
-
Li, Kam Cheong and Wong, Billy Tak-Ming
- Abstract
Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.
- Published
- 2022
- Full Text
- View/download PDF
3. EdMedia 2018: World Conference on Educational Media and Technology (Amsterdam, The Netherlands, June 25-29, 2018)
- Author
-
Association for the Advancement of Computing in Education and Bastiaens, Theo
- Abstract
The Association for the Advancement of Computing in Education (AACE) is an international, non-profit educational organization. The Association's purpose is to advance the knowledge, theory, and quality of teaching and learning at all levels with information technology. "EdMedia + Innovate Learning: World Conference on Educational Media and Technology" took place in Amsterdam, The Netherlands, June 25-29, 2018. These proceedings contain 308 papers, including 14 award papers. The award papers cover topics such as Open Education Resources (OER) certification for higher education; a cooperative approach to the challenges of implementing e-assessments; developing an e-learning system for English conversation practice using speech recognition and artificial intelligence; the Learning Experience Technology Usability Design Framework; developing strategies for digital transformation in higher education; pre-service teachers' readiness to use Information and Communication Technology (ICT) in education; teacher development through technology in a short-term study abroad program; Austria's higher education e-learning landscape; a digitised educational application focused on the water cycle in nature carried out in a secondary school in Ireland; evaluative research on virtual and augmented reality for children; how children use computational thinking skills when they solve a problem using the Ozobot; a strategy to connect curricula with the digital world; the learning portfolio in higher education; and adult playfulness in simulation-based healthcare education. [For the 2017 proceedings, see ED605571.]
- Published
- 2018
4. Comparison and Enhancement of Machine Learning Algorithms for Wind Turbine Output Prediction with Insufficient Data.
- Author
-
Im, Subin, Lee, Hojun, Hur, Don, and Yoon, Minhan
- Subjects
WIND power ,WIND turbines ,MACHINE learning ,RENEWABLE energy sources ,WIND forecasting ,POWER resources ,INDEPENDENT system operators - Abstract
As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. Making regulation flexible for the governance of disruptive innovation: A comparative study of AVs regulation in the United Kingdom and South Korea.
- Author
-
Hong, Seung-Hun, Lee, Jonghan, Jang, Sanghoon, and Hwang, Ha
- Subjects
DISRUPTIVE innovations ,GOVERNMENT regulation ,REGULATORY reform ,ARTIFICIAL intelligence ,AUTONOMOUS vehicles - Abstract
Many governments find it challenging to set up a regulatory regime to govern rapidly developing Autonomous Vehicles (AVs) technologies empowered by Artificial Intelligence. This paper analyzes flexible regulation as a tool for assessing regulatory reforms that govern disruptive innovations such as AVs. After defining flexible regulation as regulation that gives regulated entities choices for how to comply with the regulatory objectives, this paper develops Regulatory Flexibility Indicators (RFI) for rule structure, enforcement structure, and regulatory feedback. We study AVs regulatory reforms that took place recently in the United Kingdom and South Korea, focusing on how such reforms have enhanced regulation flexibility. This paper finds that regulatory governance in the United Kingdom is more flexible than in South Korea, indicating aspects of further reforms for improving regulatory flexibility. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model.
- Author
-
Na, Seunguk, Heo, Seokjae, Choi, Wonjun, Kim, Cheekyung, and Whang, Seoung Wook
- Subjects
TECHNOLOGY Acceptance Model ,ARTIFICIAL intelligence ,CONSTRUCTION industry ,SOCIAL skills ,SOCIAL influence - Abstract
The research has chosen the workers in construction-related companies in South Korea and the United Kingdom (UK) as research subjects in order to analyse factors that influence their usage intention of Artificial Intelligence (AI) based technologies. The perceived usefulness had a positive impact (+) on technological satisfaction and usage intention in terms of the commonalities shown by the construction industry workers in both countries, South Korea and the UK, in adopting AI-based technologies. Moreover, the most remarkable differences were personal competence and social influence when choosing AI-based technologies. It was analysed that in the case of South Korea, personal competence had a positive impact (+) on perceived ease of use, whereas the UK had a positive impact (+) on perceived usefulness and perceived ease of use. This study holds particular significance in the domain of cross-cultural research within the construction industry. It conducts an analysis of the factors influencing the adoption of AI-driven technologies or products, with a specific focus on the cultural differences between two nations: South Korea and the UK, which represent Eastern and Western cultural paradigms, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
7. Potential Liability Issues of AI-Based Embedded Software in Maritime Autonomous Surface Ships for Maritime Safety in the Korean Maritime Industry.
- Author
-
Kim, Daewon, Lee, Changhee, Park, Sungho, and Lim, Sangseop
- Subjects
MARITIME safety ,MARITIME shipping ,ARTIFICIAL intelligence ,SHIPBUILDING ,PRODUCT liability ,MARINE accidents ,COMPUTER software - Abstract
Maritime Autonomous Surface Ships (MASS), an emerging area of digital advancement in shipping and shipbuilding industries, presents a different legal paradigm from that of existing ships. Existing maritime-related industries, including shipping, shipbuilding, and logistics, based on large hardware called ships, are rapidly changing into highly autonomous software-centered structures. This study is focused on analyzing the legal issues in preparation for MASS' commercial operations in the future by applying comparative methods centered on the Republic of Korea and the United Kingdom. The study's results contribute to the criteria for the manufacturing responsibility of autonomous ship-embedded software and a desirable legal policy improvement plan. Various legal issues and macro legal policy directions related to software product liability were identified. The study also presents concrete implementation strategies to achieve an ideological harmony and a balance between equitable damage relief and the advancement of related technologies to ensure maritime safety in the maritime industry. Based on the issues identified and their legal policy alternatives, it is hoped that the institutional ideal of product liability, which promotes technological advancement and protects consumer rights, is realized in the software domain as well. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.