An emerging – Social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high effective next generation learning systems for the knowledge society

Lytras, Miltiadis D.; Mathkour, Hassan; Abdalla, Hassan I.; Alhalabi, Wadee; Yáñez-Márquez, Cornelio; Siqueira, Sean W. M.

doi:10.1016/j.chb.2015.06.004

Cited by 56 publications

(21 citation statements)

References 8 publications

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“…There are also a number of studies on social network-based learning innovation. In 2015, Lytras et al [16] provided an overview on the New Generation Collaborative Learning Systems, focusing on new philosophical paradigms for learning innovation in social networks, as well as the requirements for next-generation learning systems. Marques et al [17] studied the use of social networks in the context of formal learning and presented an investigation on the structure of messages on blogs and microblogs, as well as their importance in collaborative learning.…”

Section: Knowledge Sharing In Social Networkmentioning

confidence: 99%

User Willingness toward Knowledge Sharing in Social Networks

et al. 2018

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Social networks introduce new potential for people to share knowledge with others. However, it is not clear what factors influence user willingness toward knowledge sharing in social networks. Aiming to answer these questions, in this paper we analyze the major factors influencing user willingness toward knowledge sharing in social networks and propose a new research model that is inspired by the technology acceptance model (TAM). In particular, we introduce a new independent variable called perceived value which is described by four aspects: social value, entertainment value, emotion value, and information value. In addition, we introduce a new mediating variable, trust, to reflect the intermediating relationship between perceived value and knowledge-sharing willingness. We conduct an empirical analysis on questionnaire data and present comprehensive results on reliability and validity, factor analysis, correlation analysis, and mediating effects analysis. The results show that perceived value has a significant impact on knowledge-sharing willingness, and trust plays a partial intermediate role between perceived value and knowledge-sharing willingness. Further, we present some research implications for knowledge sharing and learning innovation in social networks, as well as some suggestions for organizations to advance knowledge sharing and learning innovation in the social-network age.

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Section: Knowledge Sharing In Social Networkmentioning

confidence: 99%

User Willingness toward Knowledge Sharing in Social Networks

et al. 2018

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“…Emerging research areas like cognitive computing [2] combined with artificial intelligence and machine learning, permit advanced and sophisticated methods for processing data, including sentiment analysis, image processing, natural speech recognition and text mining. In parallel emerging technologies, including cloud computing, internet of things and virtual reality, the value proposition of application and services that process data in different formats such as text, images, videos, microcontents in social media is further enhanced [3,4]. The development of a huge data ecosystem around the globe, in which providers and users of data promote business value in terms of data and decision making, is a key development of our times.…”

Section: Introductionmentioning

confidence: 99%

“…Consider social networks research and the contribution of social networks to the big data ecosystem [2,3]. Other examples are artificial intelligence and machine learning applications in various domains, such as customers/clients of big data repositories for personalized and targeted services [3,4].In the recent literature of big data research, an increasing section is dedicated to the capacity of big data to support social sciences research. There is the anticipation that big data is potentially a social good that must be secured and be used for the transparency of services, and for the evolution of a user-centric new culture for sustainable computing.…”

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Big Data and Their Social Impact: Preliminary Study

Lytras

Visvizi

2019

Sustainability

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Big data is the buzz-word of today, and yet their specific impact on individuals and societies remains assumed rather than fully understood. Clearly, big data and their use have already given rise to a number of questions, including those of how data can be collected and used in ethical and socially sensitive ways. Building on these points, the objective of this study was to explore how precisely big data and big data based services influence individuals and societies. This paper elaborates on individuals’ perceptions of data, especially on how they perceive the actual sharing of their data. In this way, this paper defines a value space for the social impact of big data relevant to three factors, namely the intention to share personal data, individual’s concerns, and social impact of big data.The main contribution of this study consists of the insights into the still nascent area of research that unfolds at the cross-section of social science and computer science. We expect that in the next years this area of research will gain prominence.

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“…There is no direct definition of AI or a consensus thereon, and AI is often understood as a set of techniques designed to use machines to approximate certain aspects of human or animal cognition. Early theorists believed that the symbolic system (the organization of related to AI, as well as many journal articles, research topics, and research conferences on AI [25][26][27][28][29][30][31][32][33][34][35]. The special issue on "Human Centered Web Science" from the journal World Wide Web is to explore how humans could keep up with the current trend toward authorizing users to collectively decide on the usage of web-based information and services in the new era of Internet and AI, and to study and discuss how to master human-driven features of Web-based systems, conduct high-level governance policies and so on [36].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019

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In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009)(2010)(2011)(2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013)(2014)(2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016)(2017)(2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.abstract symbols using logical rules) was the most productive way to pursue a computer that could "think"; however, as originally unimagined by Turing and others, the strategy of constructing an inference engine did not achieve the initial cognitive tasks, and it seemed that the theoretically possible concepts had not produced many feasible applications in practice [1]. Li and Wang gave five common definitions of artificial intelligences (AI) in their book Artificial Intelligence: (1) AI is a computer program which makes people feel inconceivable; (2) AI is a computer program similar to human thinking;(3) AI is a computer program similar to human behavior; (4) AI is a computer program that can learn; (5) AI is a computer ...

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An emerging – Social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high effective next generation learning systems for the knowledge society

Cited by 56 publications

References 8 publications

User Willingness toward Knowledge Sharing in Social Networks

User Willingness toward Knowledge Sharing in Social Networks

Big Data and Their Social Impact: Preliminary Study

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

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