Revising the Framework of Knowledge Ecologies: How Activity Patterns Define Learning Spaces

Revising the Framework of Knowledge Ecologies: How Activity Patterns Define Learning Spaces

Kai Pata (Tallinn University, Estonia)
DOI: 10.4018/978-1-60566-826-0.ch014
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Abstract

This chapter describes the Web of social software tools with its inhabitants as an evolving and ecological environment, discussing and elaborating the connectivist framework coined by George Siemens in his book Knowing Knowledge. This new perspective to ecological learning in social software environments resides on the ideas of Gibson’s and his followers approach to ecological psychology, the rising theory of embodied simulation and Lotman’s theory of cultural semiosis. In the empirical part of the chapter, we focus on the methods of investigating how social software systems become accommodated with their users forming learning spaces. Analysis discusses such ecologically defined spaces for individual and collaborative learning.
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Introduction

Recently, the widespread public use of social software in Web has triggered for the need to theoretically ground the learning phenomena in this new environment using the ecological view. Favouring the biological human-centred understanding of information systems, Davenport and Prusack (1997, p. 11) primarily used the information ecology as a metaphorical term to capture holistic and human-centred management of information. Next, the knowledge ecology and knowledge ecosystem terms were coined, which started to mark the rapidly developing area that binds knowledge creation and utilization with the social and management aspects in human networks (Pór & Malloy, 2000; Pór & Spivak, 2000). The Web visionaries like John Seeley Brown (1999; 2002), and George Siemens (2005; 2006) related knowledge ecology and knowledge ecosystem terms with weaving information and artefacts, meanings and knowledge, networks and connections. G. Siemens published a book “Knowing Knowledge” (2006), which received wide public recognition in social Web communities. He suggested Connectivism as the learning theory for new Digital Age. While the book captures a new knowledge ecology vision, it has yet several limitations, which will be discussed in this chapter.

G. Siemens formulated that Connectivism is the assertion that learning is primarily a network-forming process (Siemens, 2006, p. 15). He relies on the ideas of Downes (2005) who wrote that: A property of one entity must lead to or become a property of another entity in order for them to be considered connected; the knowledge that results from such connections is connective knowledge. The act of learning is one of creating an external network of nodes – where we connect and form information and knowledge sources (Siemens, 2006, p. 29). Connectivism focuses on the knowledge, situated externally from people in the web. Several authors address this knowledge using different terms e.g. cultural knowledge (Heft, 2001); semiotic niche (Hoffmeyer, 1995) or cognitive niche (Magnani, 2008; Magnani & Bardone, 2008). These terms will be elaborated in the further parts of the paper.

G. Siemens (2005; 2006) assumes that creating meanings and relations publicly in social software environments would aid through connective processes the formation of new knowledge ecologies and learning cultures. In the Connectivism framework Siemens takes an approach that is strongly tilted towards knowledge, meanings, communities and networks and their spaces – knowledge ecosystems. However, the Connectivism framework is inconsistent in elaborating the ecological role of tools, activities, and communities in the formation and evolvement of knowledge ecologies. Siemens writes: The pipe is more important than the content in the pipe. ‘Know where’ and ‘know who’ are more important today that ‘knowing what’ and ‘how’ (Siemens, 2006, p. 32). In this chapter we attempt to argue against this metaphoric claim. We suggest that the use of static ‘pipe’ metaphor, and diminishing the role of activities, the ‘knowing how’ part, may theoretically lead to losing the ecological nature of knowledge ecologies framework.

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