In this engaging volume, Jon Dron views education, learning, and teaching through a technological lens that focuses on the parts we play in technologies, from language and pedagogies to computers and ...regulations. He proposes a new theory of education whereby individuals are not just users but co-participants in technologies—technologies that are intrinsic parts of our cognition, of which we form intrinsic parts, through which we are entangled with one another and the world around us. Dron reframes popular families of educational theory (objectivist, subjectivist, and complexivist) and explains a variety of educational phenomena, including the failure of learning style theories, the nature of literacies, systemic weaknesses in learning management systems, the prevalence of cheating in educational institutions, and the fundamental differences between online and in-person learning. Ultimately, How Education Works articulates how practitioners in education can usefully understand technology, education, and their relationship to improve teaching practice.
Governments are evolving new ways to think, combining observation, memory, analysis, models, and creativity. This article describes how they think, how the COVID crisis has accelerated innovation in ...new ways of thinking, the use of metaphors to understand these processes, the role of democracy and civil society, and the new skills needed.
•Technological innovation has a profound impact on entrepreneurship and venture creation.•A new model of Digital Entrepreneurship Ecosystem (DEE) can be investigated.•DEEs are shaped by the ...affirmation of digital technologies as enablers and/or outputs.•Collective Intelligence provides a framework to describe the building blocks of a DEE.•Success cases can be found of process-driven, resource-driven, and product-driven DEEs.
Digital technologies have nowadays a significant impact on how new business ventures are imagined and created. The arising technology paradigm is leveraging the potential of collaboration and collective intelligence to design and launch more robust and sustainable entrepreneurial initiatives. However, although the topic of digital entrepreneurship is relevant and timely, there is a limited literature discussion on the real impact of digital technologies and collaboration on the entrepreneurial process. Further research is needed to describe the nature and characteristics of the entrepreneurial ecosystem enabled by the new socio-technical paradigm. Based on extant literature, this article proposes a definition of digital entrepreneurship ecosystem by highlighting the integrated digital-output and digital-environment perspectives. A collective intelligence approach is then adopted to define a descriptive framework and identify the distinguishing genes of a digital entrepreneurship ecosystem. Four dimensions associated to digital actors (who), digital activities (what), digital motivations (why), and digital organization (how) are defined and discussed. The framework was also applied to describe 9 real cases of companies and initiatives, which are analyzed as digital entrepreneurship ecosystems along the four key dimensions presented. The article ends with a discussion about the results and a research agenda for future studies.
Collective Intelligence and Group Performance Woolley, Anita Williams; Aggarwal, Ishani; Malone, Thomas W.
Current directions in psychological science : a journal of the American Psychological Society,
12/2015, Letnik:
24, Številka:
6
Journal Article
Recenzirano
We review recent research on collective intelligence, which we define as the ability of a group to perform a wide variety of tasks. We focus on two influences on a group's collective intelligence: ...(a) group composition (e.g., the members' skills, diversity, and intelligence) and (b) group interaction (e.g., structures, processes, and norms). We also call for more research to investigate how social interventions and technological tools can be used to enhance collective intelligence.
The presence of Collective Intelligence engenders the integrity of the person and the group in the business and thus, synergistically strengthens the collective resilience, understood as the ...individual and the company’s resilience. Synergistically as shown, is the habit of creative interactively cooperation, so that each element or party increases the effect of the other.
The fitness of an organism can be strongly affected by the decisions that it makes throughout its lifetime. These decisions may be spatial (deciding where to go), temporal (deciding when to perform ...an action), or a mixture thereof.How organisms make spatial or temporal decisions should involve different mechanisms because of fundamental differences between the two. For example, time is irreversible, while animals can traverse space more freely.Making decisions together as a group can improve the accuracy of decisions (a form of collective intelligence). However, to date, almost all existing research has been on collective spatial decisions and, as a result, it is through this spatial lens that our intuition of collective decisions has developed.Understanding how individuals in groups make timing decisions is particularly relevant in a changing climate, where both the optimal time to perform actions and the cues used to time the action are changing.Studying collective intelligence in the context of timing decisions will reveal novel mechanisms that social animals across taxa (including humans) use, allowing us to predict the future of species in a changing world and to design new bio-inspired strategies.
The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed–accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.
The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed–accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.
In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view ...representation learning, which can learn a unified representation from multiple data sources, is a key pre-task of multi-view learning and plays a significant role in real-world applications. Accordingly, how to improve the performance of multi-view representation learning is an important issue. In this work, inspired by human collective intelligence shown in group decision making, we introduce the concept of view communication into multi-view representation learning. Furthermore, by simulating human communication mechanism, we propose a novel multi-view representation learning approach that can fulfill multi-round view communication. Thus, each view of our approach can exploit the complementary information from other views to help with modeling its own representation, and mutual help between views is achieved. Extensive experiment results on six datasets from three significant fields indicate that our approach substantially improves the average classification accuracy by 4.536% in medicine and bioinformatics fields as well as 4.115% in machine learning field.
Although blockchain has attracted a great deal of attention from academia and industry there is a lack of studies on acceptance drivers. This study explores blockchain acceptance by mining the ...collective intelligence of users on Twitter. It maps blockchain user acceptance drivers to technology acceptance constructs. The analysis shows that users are attracted by security, privacy, transparency, trust and traceability aspects provided by blockchain. On Twitter more discussions on blockchain benefits than on drawbacks. Initial coin offering (ICO) is extensively discussed. The study provides guidelines for managers and concludes by presenting the limitations of the study along with future research directions.
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