Archive for September, 2008


Elaborating connectivism

September 28, 2008

This chapter draft 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 the Lotman’s ideas from cultural semiotics.

The full chapter is published in the Educational social software for context-aware learning book.

Full chapter

1. 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. The theoretical framework developed by Web visionaries like John Seeley Brown (1999; 2002), George Siemens (2005; 2006) and others is directed towards information and artefacts, meanings and knowledge, networks and connections, in weaving ecologies of knowledge. In his book “Knowing Knowledge” George Siemens (2006) has suggested Connectivism as the learning theory for new Digital Age. He 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. 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 George 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.

Studies of communities and networks assume that these are formations of people (Lin, Sundaram, Li, Tatemura & Cheng, 2006; Kumar, Novak & Tompkins, 2006) or their artifacts (Klamma, Spaniol, Cao & Jarke, 2006). What yet is missing is seeing Web 2.0 as a united ecological system with its inhabitants. The interrelations between communities, the environment and the culture left there by people – the traces of meanings (Lor, Yesui, Welge & Goldberg, 2006) and the traces of activities – are important in the ecological framework. Similar tiltedness towards artifacts and meanings appears in the development of most of the social software tools. In social software systems we can find several possibilities of organising and filtering content by socially defined meanings, however, to see what activities take place in the communities that use these systems is often possible only if participating in the communities. We assume in this chapter that the ecological formation of common places, where communities and networks exist and take action, needs to be integrated into the theoretical explanations about connectivist learning in these systems.

2. Knowledge ecosystems in the Connectivism framework: flowing knowledge in the connected pipes

Siemens (2006) wrote in his book Knowing Knowledge that Connectivism is a staged view of how individuals encounter and explore knowledge in a networked/ecological manner. The central concepts Siemens discusses are: knowledge, learning, spaces, networks and knowledge ecosystems. He illustrated his framework with the following metaphor: 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’ (p. 32). Subsequently we introduce his position in concerns of these terms, asking also some questions, which reveal the areas where Connectivism framework must be elaborated. These questions will be further elaborated in the next chapters.

Knowledge: Knowledge rests in networks. Knowledge may reside in non-human appliances, and learning is enabled/facilitated by technology (p. 31). The act of knowing is offloaded onto the network itself – to a connected network of specialists. The network (or web) of connections is the structure, which holds the knowledge of individuals in a holistic manner (p. 33). Content is imbued with new meaning when situated in network (or is more accurate to say that the network acquires new meaning when new content is added?) (p. 43).

Learning: Learning is a network formation process of connecting specialized nodes or information sources (p. 31). The elements that create understanding are scattered across many structures and spaces. We ‘know’ when we seek and pull elements together – when we create a meaning-laden view of an entity (p. 45).

Q: How technology enables/facilitates ecologies?
Q: How network holds knowledge and acquires new meanings?

Spaces: We create spaces where we can dialogue about and enact knowledge (p. 4). Ecologies and networks provide the solution to needed structures and spaces to house and facilitate knowledge flow (p. 86). Understanding knowledge in a particular era is important in ensuring that we have aligned our spaces and structures with the nature of knowledge (p. 10). Spaces are themselves agents for change. Changed spaces will change practice (p. 87).

Q: How spaces enable enacting knowledge?
Q: Can we separate the knowledge flow from the structures and spaces – networks and ecologies – where knowledge flows?

Networks: Our mind is a network… and ecology. It adapts to the environment (p. 27). The network is a structure that individuals create on their own (p. 132). Content is imbued with new meaning when situated in network (or is more accurate to say that the network acquires new meaning when new content is added?) (p. 43). Better quality of networks and connections result in better quality knowledge sharing (p. 20). Networks occur within ecologies. Ecology is a living organism. It influences the formation of the network itself. The health of each personal learning network is influenced by the suitability of the ecology in which the learner exists (p. 92).

Q: What are networks: Personal learning environments (PLEs)? Connections between artifacts a person creates? Connections between people a person interacts with?

Ecology: Ecologies and networks provide the solution to needed structures and spaces to house and facilitate knowledge flow (p. 86). Ecology is a knowledge-sharing environment (p. 87). The ecology fosters connections to original and knowledge sources, allowing for currency. The ecology fosters rich interaction between disparate fields of knowledge, allowing growth and adaptation of ideas and concepts. Each participant in the ecology pursues his/her own objectives, but within the organized domain of knowledge of a particular field (p. 117). Ecologies permit diverse, multi-faceted concepts… and meanings to emerge based on how items are organized or self-organize (p. 87). The creation of the ecology permits a broad-scale implementation of differing knowledge and learning experiences, permitting employees to achieve knowledge-based needs in a multi-faceted manner, multiple ways, and through multiple devices (p. 132). Ecologies are nurtured and fostered…instead of constructed, organized and mandated (p. 90). Ecologies are capable of rapid growth, adapting to new competition, differing perspectives, and enabling innovative concepts and ideas to gain traction (p. 87). Ecology is a living organism (p. 92). Ecologies are: loose, free, dynamic, adaptable, messy, and chaotic (p. 90). The ecology influences the formation of the network itself. The health of each personal learning network is influenced by the suitability of the ecology in which the learner exists (p. 92).

Q: How does the ecology influence personal networks?

Siemens (2006, p. 87) also discusses the characteristics of ecologies that promote knowledge sharing. He emphasizes the freedom of choice to use different systems and tools that meet the needs of each person, and which they perceive easy to use. This suggests owning a personal learning environment (PLE), which is accommodated to certain person’s needs and is interconnected with other person’s PLEs. Secondly, the variety of systems and tools that individuals use is considered important. This may increase the possibility of making connections between people and between artifacts across the various borders. The personal choice in making connections is of importance to hold motivation and inquiry spirit. Because knowledge is supposed to situated in networks and connections, the deep and trusting connections between individuals, who uptake knowledge from the ecologies, and tolerance among these individuals must be achieved. Thirdly, the consistency of participating in certain practices with knowledge is suggested, which may increase the probability that patterns will emerge within ecologies, and that persons will notice them.

Individuals are active in the learning ecology/space in terms of consuming or acquiring new resources and tools. The learner begins to actively contribute to the network/ecology essentially, becoming a visible node. Time in the network has resulted in the learner developing an increased sense of what is happening in the network/ecology as a whole. She/he will become more adept at recognizing new patterns or changing winds of information of knowledge. Individuals are capable of understanding what do the emerging patterns mean. The learner is also focused on active reflection of the shape of the ecology itself. The learner may engage in attempts to transform the ecology beyond his/her own network (Siemens, 2006, p. 45).

In the practical implementation of Connectivism ideas into learning Siemens (2006, p. 140) suggests three key aspects of ecologies – they must be holistic, adaptive and result-focused. These concepts may also serve as the starting-points into taking the fresh look at the knowledge ecologies.
Siemens suggests that holistic ecologies represent the situation diversely, allowing multiple perspectives and views. We can further argue that holistic view means that we may find several subspaces in the ecologies, which differ from each other by perspectives. Ecologies are formed of many individuals who try to realize their personal objectives, often individually and without being consciously involved into group actions. The view at the ecology level permits to see these individuals forming various communities who share similar views or act in a certain way without even knowing each other or forming networks. However, the communities inhabit spaces in the ecology, which are evolving, and dynamically changing. Across the vaguely defined borders of community spaces, knowledge can be interpreted and translated, creating new knowledge. The abstract space concept, which we formulize as a niche for certain community, is central in the revisions of Connectivism framework.

Secondly, Siemens suggests that ecologies must be adaptive and able to adjust and change as the environment changes. These characteristics are elaborated further in the next subchapter, introducing the ecological idea of affordances that define niches. If the persons are linked to their existing habits, activities, processes and tools, like Siemens suggests, any change in their objectives and preferences would cause the changes in the whole environment, in these communities. People, activities, and tools what they find to fulfill their objectives are ecologically interrelated. People rely on the cultural behaviors that take place in certain social environments – eg. tagging of personal meanings or reflecting in public spaces etc. Thus, community activities influence, which perspectives of meanings, actions and tool functionalities and objectives would be actual for the learners. Everything what people do, remains as the feedback into the systems. It is interpreted as ecological knowledge, influencing not only this community, but also potentially other communities.

Thirdly, Siemens emphasizes the intended targets and desired outcomes that the ecologies might have. This view would obtain a new meaning if we stop seeing the formation of ecologies as the systems purposefully designed by groups, but as emergent and evolving activity systems. The mutual interrelations between individuals, their objectives, and what they see and use in the surrounding system, when constructing knowledge, are triggered ecologically. The ecological knowledge is always being formed and always influencing what is being formed, and how it is being formed. This ecological knowledge is not only content left into the systems, but also the process traces from actions taken with certain tools with certain artifacts, with certain people. Thus, the communities always shape their spaces and these spaces shape the communities.

3. Enactment when learning in knowledge ecosystems: communities construct niches

In the previous discussion several questions where raised when analyzing the Connectivism framework. These questions revealed that there are unclear aspects, suggesting the necessity to take a more in-depth look into the knowledge ecologies. We have reorganised the order of these questions to frame our argumentation about the nature of knowledge, networks, knowledge ecologies and their interrelations.

Q: What are networks: Personal learning environments (PLEs)? Connections between artifacts a person creates? Connections between people a person interacts with?
Q: How network holds knowledge and acquires new meanings?
Q: Can we separate the knowledge flow from the structures and spaces (networks and ecologies) where knowledge flows?
Q: How technology enables/facilitates knowledge ecologies?
Q: How spaces enable enacting knowledge?
Q: How does the ecology influence personal networks?

To answer these questions we introduce some more concepts to the ecological learning framework: niches as abstract community spaces, affordances that define niches, ecological knowledge as the feedback that communities create, enactment and embodied simulation as the possible processes that cause ecology formation. Siemens (2005, 2006) has built his Connectivist framework on the ecological understanding. However, deepening of the ecological approach enables to see activities in the more central position in the knowledge ecologies. We aim to elaborate the knowledge ecosystem idea, strengthening the role of activities and introducing the theoretical framework how activities are related to the knowledge ecosystems.

Knowledge ecologies framework

Knowledge ecologies framework

Network-knowledge interrelations

Q 1: What are networks: Personal learning environments (PLEs)? Connections between artifacts a person creates? Connections between people a person interacts with?

We do not want to criticise the main idea expressed by Siemens (2006) in Knowing Knowledge, declaring that knowledge rests and changes in the networks that connect people and their artifacts. Asking, what these networks are, we want to emphasise the role of tools and activities as an ecologically entwined parts of the network. We emphasise that knowledge, is more than information and meanings – knowledge has an activity- and tool-related dimension. Personal learning environments (PLEs) that people construct and use in their daily activities are not merely the mediators, ‘the inactive pipes’ that enable knowledge flow. PLEs are dynamically evolving activity systems in which the personal objectives and human and material resources are integrated in the course of action.

We want to emphasize the distributed nature of what we define as PLEs. Here, we do not mean only that each PLE may be constructed of many separate tools forming a distributed system. PLE is also distributed ecologically, integrating our minds with the environment. Hommel (2003), has written that action control to all behavioral acts is ecologically delegated to the environment – when planning actions in terms of anticipated goals, the sensory-motor assemblies needed to reach the goal are simultaneously selectively activated in the environment, and bind together into a coherent whole that serves as an action-plan, facilitating the execution of the goal-directed actions through the interaction between the environment and its embodied sensory-motor activations. In the frames of ecologically defined learning systems, we can assume that our embodied sensory-motor knowledge of previous meaningful actions and its environmental correlates that we find around us form one emergent distributed system. In the course of learning our PLE is always in change. We actualize certain dimensions from the environment around us integrating it to the action-plans, and simultaneously the environment extends certain dimensions to us changing and shaping our intentions. Deliberately, we do not talk of the environment as merely of tools and systems. Environment involves all kind of resources in PLEs – people, artifacts, software systems and services. Thus, the network in the ecological framework may be interpreted as a distributed system continuously constructed of our minds and the environment components.

Q 2: How network holds knowledge and acquires new meanings?

Applying the previous interpretation of networks in ecological framework we can assume that knowledge that the networks hold is pattern-like, distributed between the environment and people, and dynamically emergent in activities. Two perspectives are important about the nature of knowledge – knowledge is always developed within the distributed systems personally and culturally. These personal and cultural ways to create knowledge are interrelated.

Varela, Thompson & Rosch (1991, p. 149) wrote that knowledge is the result of ongoing interpretation that emerges from our capacities of understanding. These capacities are rooted in the structures of our biological embodiment but are lived and experienced within a domain of consensual action and cultural history. They coined the term embodied action to transmit the idea that cognition depends upon the kinds of experience that come from having a body with various sensorimotor capacities, and second that these individual sensorimotor capacities are themselves embedded in a more encompassing biological, psychological, and cultural context. The authors assumed that sensory and motor processes, perception and action are fundamentally inseparable in lived cognition (p. 172-173). Using the term enaction they focused on two points: 1) perception consists of perceptually guided action, and 2) cognitive structures emerge from recurrent sensorimotor patterns that enable action to be perceptually guided (Varela et al, 1991, p. 173).

Bereiter (2002, p. 57) framed and answered the question about the nature of knowledge as follows: Where is knowledge if it isn’t contained in individual minds? The kind of answer coming from activity and situated cognition theorists runs along the following lines: Knowledge is not lodged in any physical or metaphysical organ. Rather knowledge inheres in social practices and in the tools and artifacts used in those practices. Knowledge is regarded as distributed. This does not mean merely that it is spread around, a bit here and a bit there… knowledge does not consist of little bits…all the knowledge is in the relationships – relationships among the people engaged in an activity, the tools they use, and the material conditions of the environment in which action takes place. Yet, Varela et al. (1991) and Bereiter (2002) do not offer explanations of how network holds knowledge and how this knowledge can change.

Recently researchers have come up to the idea how this distributed knowledge emerges as a result of embodied simulation. Discoveries in cognitive and neuroscience about the functioning of mirror-neuron systems (Gallese et al., 1996), claim, that cognition is embodied through grounding knowledge directly in sensory-motor experiences without the mediation of symbolic representations (Pecher & Zwaan, 2005). Research indicates that from observation of others and the environment (Rizzolatti et al., 2001), from listening narratives (Rizzolatti & Arbib, 1998; Iaccoboni, 2005) or from reading narratives (Scorolli & Borghi, 2007) and looking everyday images of objects or works of art (Gallese & Freedberg, 2007) we perceptually activate certain multi-modal action-potentialites of embodied symbols that mediate our purposeful and goal-directed actions (see Gallese & Lakoff, 2005). When acting in social learning environments not only the meanings are newly created from found information, but also the action-related cues are picked up from different narratives and from the whole systems, and they are integrated into our action plans. These findings indicate, that besides possibilities of organizing meanings with various ways in social learning environments, much more attention needs to be put on these action-related cues individuals and communities interact with in the environment. Knowledge is always in change because of personal nature of embodied simulation processes and the influence or feedback that people make with their actions, action- and meaning traces, and their specific way of activation of PLEs on the environment, to other people.

Q 3: Can we separate the knowledge flow from the structures and spaces (networks and ecologies) where knowledge flows?

In the revised ecological framework knowledge and networks are integrated. Our ecological learning framework binds together three assumptions: i) network may be interpreted as a distributed system continuously constructed of our minds and the environment components in the course of action; ii) knowledge is pattern-like, distributed between the environment and people, and is dynamically emergent in activities, iii) knowledge emerges as a result of embodied simulation, when people perceptually activate certain multi-modal action-potentialites from the environment that mediate their purposeful and goal-directed actions, and leave action- and meaning traces as a feedback to the environment.

Network-ecosystem interrelations

Q: How technology enables/facilitates knowledge ecologies?

The concept of ecology plays an important part in the Connectivism framework of learning. However, Siemens (2006) is not very precise in explaining how ecologies and networks influence each other. In our main amendments to Connectivism we try to elaborate the emergent nature of ecologies with bottom-up social definition of learning niches, discuss what is the role of using software systems in these ecologies, and describe how feedback through ecological knowledge connects ecologies and networks.

Ecologies are formed as a result of many individuals taking actions. Thus, people with various perspectives are simultaneously at present in these ecologies and influencing them. Many abstract subspaces can be formed within ecologies. Such spaces emerge when parts of the environment are embodied and used similar way by many people. Spaces are more general than networks of one individual – they come to existence and can be identified only if many individuals actualize similar personal learning environments for the same purposes with certain frequency at the certain period of time. These groups of individuals have something in common in their identity. They form communities who inhabit the same abstract learning spaces in the ecology – niches.

The formation of learning spaces as niches for specific learning-related activities happens through the social definition of several factors that influence learning. Hutchinson (1957) defined a niche as a region (n-dimensional hypervolume) in a multi-dimensional space of environmental factors that affect the welfare of a species. He also made difference of fundamental and realized niche – the former exist as the complex of all necessary environmental characteristics for certain species, the latter is formed under the pressure of all the currently available environmental characteristics in the competitive conditions with other species.

Niches have been conceptualized as the environmental gradients with certain ecological amplitude, where the ecological optimum marks the gradient peaks where the organisms are most abundant. In the gradient concept structural ecosystem properties are comprehended as concentration gradients in space and time (Müller, 1998). Any niche gradient is a peak of the fitness landscape of one environmental characteristic (Wright, 1931), which can be visualized in two-dimensional space as a graph with certain skew and width, determining the ecological amplitude. The shape of the fitness graph for certain characteristic can be plotted through the abundance of certain specimen benefitting of this characteristic. All niche gradients are situated and establish a multi-dimensional hyper-room, which axes are different environmental parameters. Thus, any learning niche in social systems is determined as a set of characteristics that people perceive and actualize as useful for their activities and wellbeing individually or in groups. Each niche gradient defines one dimension of the space. The fundamental niche term applies for all the possibly usable software tools and services, artifacts and people, while the realized niches form under the constrained conditions of resource availability.

What is the mechanism how niches appear? Previously we have described the embodied simulation as a candidate of ecological emergence of knowledge and networks. In ecological psychology and recently in learning environment design the interrelated nature of people and the environment was explained using the affordance concept. This concept we use in our elaborated framework to describe the different dimensions people actualize in the course of action with the environment.

Gibson (1979) originally defined affordances as opportunities for action for an observer, provided by an environment. The mainstream view on affordances in educational technology settings considers them as objective properties of the tools, which are perceptible in the context of certain activities. Thus, it is commonly suggested that tools have concrete technological affordances for certain performances that can be brought into a learner’s perception with specific instructions (Norman, 1988; Gaver, 1996). This use of the concept tends to ignore its relativistic nature and observer-dependence, and seems to imply that affordances should be located in the environment or specific artifacts or tools.

The interactional affordance concept that supports the embodied simulation mechanisms appears in a number of studies. Chemero (2003), a researcher from the school of Gibsonian ecological psychology, has suggested that affordances are rather the relations between particular aspects of the animal and the situations. Gaver (1996) emphasized that affordances emerge in human action and interaction and, thus, go beyond mere perception. Chemero wrote that affordances are features of whole situations (meaning the actors are part of this situation). Michaels (2003) claimed that perceiving affordances is more than perceiving relations, but it brings attention to the action-guiding information and sets up action systems to act.

Barab and Roth (2006) have noted that connecting learners to ecological networks, where they can learn through engaged participation, activates the affordance networks. Affordance networks, in contrast to the perceptual affordances described by Gibson, are extended in both time and space and can include sets of perceptual and cognitive affordances that collectively come to form the network for particular goal sets. According to Barab and Roth (2006) affordance networks are not entirely delimited by their material, social, or cultural structure, although one may have elements of all of these; instead, they are functionally bound in terms of the facts, concepts, tools, methods, practices, commitments, and even people that can be enlisted toward the satisfaction of a particular goal. In this way, affordance networks are dynamic socio-cultural configurations that take on particular shape as a result of material, social, political, economic, cultural, historical, and even personal factors but always in relation to particular functions. Barab and Roth (2006) assumed that affordance networks are not read onto the world, but instead continually “transact” (are coupled) with the world as part of a perception-action cycle in which each new action potentially expands or contracts one’s affordance network.

Affordances emerge and potentially become observable in actions what people undertake to realize their goals. Actions of other people in the environment or traces of their action serve as the triggers of new action plans. Vyas and Dix (2007) distinguished 3 levels of affordances: personal, organization/community, and culture level, which differ also on the level of how rapidly they can change. They claim that affordances of different levels influence each other. For example affordances one person can perceive may depend on the affordances the community perceives or culture uses as norms. Heft (2001) wrote that: “we engage a meaningful environment of affordances and refashion some aspects of them… These latter constructed embodiments of what is known – which include tools, artifacts, representations, social patterns of actions, and institutions – can be called ecological knowledge. Ecological knowledge through its various structural, material culture, human setting manifestations becomes an integral social and cultural part of ‘the environment’, with these social and cultural affordances constituting effective, largely material, forms of knowledge with their own functional significance, cultural transmission, and adaptation implications.” Heft’s interpretation enables to view both the information from the artifacts but also the traces of action in social software systems as important components that define knowledge ecologies.

We can conclude that we in our elaborated framework of ecological learning we support the idea that affordances are the perceived possibilities for both thinking and doing, what learners evoke and signify during their actual interaction with an artifact or tool and with each other. People determine the personal learning affordances within their PLEs. Hence, the learning affordance descriptions involve the learning action verbs, people who are involved in action, and mediators of actions (various tools, services and artifacts). Any individual conceptualizes learning affordances personally, but the range of similar learning affordance conceptualizations may be clustered into more general affordance groups eg. ‘pulling social awareness information’ or ‘searching artifacts by social filtering’ etc. These affordance clusters we may interpreted and used as the abstract learning niche gradients.The affordances as niche gradients are socially developed.

Using the affordance conception for defining learning space dimensions for the communities, we can bring the emergent ecological properties from the individual network level to the new structural level that is niches in the ecologies. Ecologies integrate many niches of different communities. The awareness of different niches is obtained by tracing the meaning-spaces and activity patterns of other people twined between the distributed real and virtual places they inhabit. If the dimensions of learning niches become unfolded they become usable for our own self-directed learning. Two aspects here are important. The meaning centred aspect suggests to use distributed PLEs to be aware of more communities and their meaning niches, and to create conditions for transferring information from one conceptual dimension to another. This precondition for cross-border meaning-building activities has been focused both in cultural semiotics as well as in the theory of Connectivism. Second aspect is finding people to learn together with. To be involved in the similar activities, similar action niches need to be used for interaction. Learning affordances enable to characterize these action niches.

Q: How spaces enable enacting knowledge?
Q: How does the ecology influence personal networks?

Previously we have defined the spaces in the Connectivism framework as learning niches. Here we assume that niches enable to enact knowledge and influence personal networks because of ecological inheritance left as feedback to the social software systems. We suggest that this ecological inheritance is the particular set of affordances and meanings left into the systems by various communities in the form of meaning- and action-relevant cues.

A recent literature in evolutionary theory emphasizes the idea of niche construction (Odling-Smee et al., 2003) as an ecological factor. It is argued, the organism has a profound effect on the very environment as a feedback loop. Organisms have influence on their environment, and the affected environment can have a reciprocal effect on other organisms of this species or on other species, creating an environment different from what it would have been before it was modified. This view challenges the convention of a distinct separation between organism and its environment. The niche-construction perspective stresses two legacies that organisms inherit from their ancestors, genes and a modified environment with its associated selection pressures. The authors assume that the feedback must persist for long enough, and with enough local consistency, to be able to have an evolutionary effect. They introduce the term ecological inheritance. Genetic inheritance depends on the capacity of reproducing parent organisms to pass on replicas of their genes to their offspring. Ecological inheritance, however, does not depend on the presence of any environmental replicators, but merely on the persistence, between generations, of whatever physical changes are caused by ancestral organisms in the local selective environments of their descendants. If organisms evolve in response to selection pressures modified by themselves and their ancestors, there is feedback in the system.

In accordance with the ecological inheritance ideas social software systems demonstrate similar interdependency between user-generated environmental influence and the development of user culture. The activities in social systems make them into the arenas of ‘produsage’ where learners’ production and consumption cannot be separated from the surrounding environment (Bruns, 2008). The concept of ‘produsage’ as a term highlights the idea of embodied action, suggesting that within the communities, which engage in the collaborative creation and extension of information and knowledge, the role of consumer and even that of end user have long disappeared, and the distinctions between producers and users of content have faded into comparative insignificance. People actively participating in social web culture and technological systems form an ecological system.

It is generally accepted that learning, and tools used by certain culture from one side, and individuals of this culture and their learning and tool-using habits from another side, are influencing and shaping each other mutually (see Vygotsky, 1978). By definition, the more social software tools are used, the better they become adjusted to the cultural habits of their users. The more user-defined interrelations between the meanings exist and can be activated by social-software, the better the systems get for social retrieval of information. The more users‘ activities in social environments are externally marked by the users, for example with machine-readable formats describing people, the links between them, and the things they create and do, the better the access to the activity-related information and people becomes. The positive side effect of it is also, that the systems obtain new qualities for monitoring and getting awareness, that would open the gateway to the otherwise non-traceble communities in which the members are not personally related into social networks through shared activities. They may or may not have an awareness of each other, but they share similar meanings or perform same type of activities. Access to such people in new environments is potentially opening a multi-dimensional place where individuals can learn from each other or where shared group activities can be initiated for learning purposes. The more people get involved into the similar activities, while evoking for themselves certain functions the social tools offer, the stronger the pressure gets of developing the systems towards facilitating this activity, and the more this activity becomes part of the learning culture in this environment. This presumes the ecological relationships between people and their objectives for action in certain learning environments, and the personally differentiated perception of meanings and tools in their surrounding environments. Such relations would alltogether dynamically shape the social software environments as places for learning.

An interesting aspect about ecological knowledge is its influence to the subsequent members of the community or other communities. Niches and their communities have interdependence and they cannot exist without each other. Besides this, some communities benefit from the niches of other communities, but these may not be existential for their wellbeing.

Vandermeer (2008) explains that if organisms construct their environments, there must be ecological consequences in addition to the evolutionary ones. He distinguishes between obligate and facultative organisms and niches, formulating assumptions how these organisms are influenced by niche construction: a) In an obligate constructive niche the organism dies in the absence of niche construction; b) In a facultative constructive niche the organism survives even in the absence of niche construction, nevertheless will benefit further from the construction, c) A facultative organism survives even in a non-constructive niche, but benefits further from the construction, and d) An obligate organism does not survive unless a constructed niche becomes available. These assumptions can be transferred to the social web environments. For example: a) Wikis and microblogging environments can be considered obligate constructive niches, where single person without the community has very little benefit of the system; while b) Blogs or social bookmarking systems may be seen as facultative constructive niches, in which keeping individual diary or collecting bookmarks gives some additional value even without the community; c) A facultative user of web systems will not rely on its’ activities on the niche construction of the other users; but d) An obligate web user has constructed its personal learning environment of community tools and services eg. of pulling feeds, and cannot function effectively without this niche construction.

4. How ecologies enable learning and knowledge?

In this chapter we try to elaborate some ideas how learning happens within ecologies. We rely heavily on the model created by Lotman (1990) of the semiosphere to explain cultural semiosis. His model depicts interrelation and semiosis as a knowledge creation between cultural spaces on the basis of meanings. Our interpretation of affordances as the equally important triggers for niche formation enables to consider that similarly to meaning-creation also the action-related information is re-interpreted and translated, creating new possibilities for enactment.

Deeley (1990) defines semiosis as a process of applying signs to understand some phenomena, reasoning from sign to sign, and intervention of new signs to make sense of some new experiences. Processes of operating within the same or between different sign systems are characteristic to problem-based learning. The simplified way of interpreting semiotic processes is by claiming that there is a complete mutual translability between signs from different systems and all the information can be transformed from one system to another without any loss until new understanding of the phenomenon under investigation has reached learners’ minds. As an improvement, Eco (2000) suggested that semiotic processes are more complex. He interpreted signs as not fixed semiotic entities but rather the meeting ground for independent elements coming from different planes and meeting on the basis of coding correlation.

A cultural semiotic Lotman (1990) assumed that separate sign systems do not have mutual semantic correspondence. Lotman (1990) wrote that any cultural semiosphere and its text-generating mechanisms depend on otherness and its semiotic input in order to forge appropriate conditions for semiotic enrichment and change. He assumed that the dynamic reconstruction of context, the alteration of meanings, and the construction of new information happens only in the communication between differences when the lack of fit between languages creates the conditions for translation. According to his theory, fixed elementary semiotic systems are abstractions. Instead, semiosphere should be regarded as an initial unit where semiotic processes take place between inconsistent semiotic spaces that people create in their minds. Lotman (1990) has defined the conception of the semiosphere as a living space of dialogical events, in which the production of consciousness and meaning can only take place through contact with an ‘Other’. He explained that during semiotic processes people always focus on those aspects of the sign systems, which are important to them. They systematize their perceptions into structured descriptions of the system, by this distinguishing also the elements that are perceived as belonging to out-of-system area or to other systems for them. Thus, a dynamic binary structure is formed in their minds that Lotman described as the semiosphere.

Binary parts of semiotic spaces – common and align contexts – are connected by translation. Stecconi (2004) suggested that during semiosis the translator relies on his notion of similarity to find and generate intuitively equivalent relations between sign systems, using abduction to make certain elements of these systems that may not have similar meanings equivalent. By this, the dynamics of semiotic structures emerge from the involvement of the out-of-system elements to the system and the upstage of the system elements to the regions with less systematic nature.

Learning ecologies are similar to the semiosphere model. Niches support the formation of binary structures, the places where learners must apply different rule-systems and languages, and can yield knowledge or find new ways how to yield knowledge. Niches enable to translate between common and align contexts not only meaning-based, but also the affordances of different niches may be integrated temporarily into ones personal learning environment for performing certain actions. The formation of the dynamic ecology for the learners depends on whether their personal learning environment evokes different affordances from different niches enabling their interrelations, and if learners perceive and start using the interrelated binary structures manifested by these emerging affordances. The software use at different, non-familiar communities may in some cases attribute totally new affordances to the software that differ from the previous cultural use of this software. One example of how this translation of affordances has appeared may be taken from microblogging environments (Mackie, 2007). Integrating one new tool may restructure the whole set of affordances people perceive in concerns of other software in their PLE.


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Cultural transmission theory..also for research about web artifacts?

September 27, 2008

Recently i found one paper elaborating Cultural transmission theory (CT) research in the anthropological sciences and outlining the benefits and drawbacks of this theoretical framework for the study of material culture. This framework seems quite interesting for explaining the phenomena in artifact culture of the web communities as well.

Most interesting aspects for me are related with my ideas of ‘learning’ in niches:

– culture constitutes a second (in addition to genes) mechanism by which inheritance occurs
This claim is well related to the niche construction ideas from other studies and the evolutionary effect of niche construction.

– our analyses should include more than one measure of relatedness of cultural patterns, each valid but representing different information pathways.
This assumption makes me think of the various gradients that define niches, and how these gradients influence each other.

– humans link the difficult information to things they can easily transmit as part of packages, and certain kinds of information will be bundled during transmission.
Is it the ‘view’ of affordances at some cases, they are somewhat externalized into cultural artifacts meaningful for the other actors coming mainly from this context?


Cultural Transmission Theory and the Archaeological Record: Providing Context to Understanding Variation and Temporal Changes in Material Culture
Jelmer W. Eerkens and Carl P. Lipo
J Archaeol Res (2007) 15:239–274

Explaining similarity and change in artifacts over time has been a long-standing goal of archaeologists.
Culture historians were interested in how these sequences varied from place to place and over time.
New focus now includes explaining other aspects about artifact variability beyond typology, including explaining why artifacts change the way they do.

Culture transmission (hereafter, CT) is simply the idea that similarity in behavior and artifacts may be caused by the exchange of information using a nongenetic mechanism.

Common descent in CT merely refers to the notion that information about material culture is passed between individuals and that similarity in artifact form may be a product of information ultimately coming from the same source.

Similarity in artifact forms over time and space was commonly explained by reference to the diffusion of ideas and information.

Diffusion was considered a general mechanism by which information was passed (or inherited) across and between populations.

Early and mid-20th century diffusion models were focused on the ‘‘culture’’ as a unit of study, and ideas were perceived as being diffused in and out of groups of people who comprise sets of bounded entities.

CT today derives from Darwinian models of evolution.
CT consists of the recognition that culture constitutes a second (in addition to genes) mechanism by which inheritance occurs.
CT theory provides a powerful means for linking measures of behavioral similarity and claims about historical relatedness.
Darwinian theory, of which modern CT is a part, is based more on the actions and decisions of individuals.
CT acts to decouple information transfer from biological reproduction and allows information to be continually passed from one organism to another through social learning.

The results of individual learning (i.e., behavior modification) can be transmitted, in the modified state, to other individuals. Through individual learning and CT, organisms can continually acquire, modify, and pass on modified information. Thus, the process of CT is fundamentally based on the interaction of both individual experimentation (i.e., innovation) and social learning (i.e., copying).

CT can create patterns in behavioral traits that are distinct from behaviors controlled and transmitted genetically.

Cultural information may consist of a single trait from a single individual, the average of a trait in a group of individuals, the modal trait in a population, or any other combination from any set of models.

We also can acquire information as traits, sets of traits, or simply as rules on how to acquire additional traits or rules.

Behaviors transmitted culturally have the potential to evolve (i.e., change) quicker than those passed on genetically because there are few limits to the structure of information inheritance in CT.

Multiple patterns will generally characterize CT.
When we study cultural variability, our analyses should include more than one measure of relatedness, each valid but representing different information pathways.

As a result, in some cases cultural variability may lack distinct groups with clear boundaries and cohesive internal information, though such groups can clearly form.

Not all similarity in cultural behavior necessarily indicates historical relatedness.
Distinguishing between instances of historical relatedness and convergence will form an important realm in future evolutionary studies.

Where and how replication takes place in CT?
The lack of attention in this area leads to fairly simplistic notions of traits moving from individual to individual with frequencies that are driven only by their prevalence in the population.

Gabora (2004), notes that the locus of cultural replication is in the minds of individuals. Minds are more than simple ‘‘bags’’ that hold traits but complex webs of algorithms and rules for acquiring and especially sorting information. Gabora calls these algorithms a worldview.

As a person receives cultural information, it is filtered through their worldview, where it is assimilated and related to all existing information before being stored and later recalled. Although strongly shaping the kinds and rates of information acquisition, worldviews are not static entities but constantly change.
The worldview not only transforms incoming information but is transformed itself to accommodate new information and is itself transmitted culturally.

First, we expect that individuals living in similar cultural, social, and physical environments will
tend to acquire similar worldviews. As a result they also may acquire similar kinds of behavioral traits, including material culture.

CT may include not only information about traits but also rules about how/when to acquire traits as well as rules about how/when to acquire new rules. This means that information may change (i.e., evolve) within a population at dramatically different rates.

Third, we expect that the set of rules that compose the ‘‘worldview’’ will be cumulative because they build on one another.

CT is a separate inheritance system that governs behavior, CT can easily account for seemingly ‘‘maladaptive’’ traits that spread or even come to dominate within the range of things people do.

For example, despite fairly strong genetically controlled instincts to eat, CT can explain why a behavior like anorexia may spread within a population (e.g., worldviews pertaining to a certain body image).

A significant amount of information is preserved in some way and is coherently passed from individual to individual through populations over relatively long periods of time. How well the historical signal of cultural transmission is preserved, particularly over long periods of time, is unknown and surely varies from context to context.

We assume that at least some information is transmitted between individuals and that this information is subject to modification before being retransmitted to others either through copying error, filtration through worldviews, or purposeful ‘‘innovation.’’ As a result, the information that is transmitted is subject to evolutionary forces.

We suggest further that at least some of this information stays relatively intact over archaeologically relevant periods of time.

‘‘Information’’ that is argued to be transmitted between individuals: Unlike DNA, which is physically passed from person to person in Genetic Transmission (GT), no such empirical entity is known for CT.

We have no direct way of ‘‘seeing’’ transmission.
There is no physical ‘‘chunk’’ of material that is passed from individual to individual.

Fundamentally, there is no distinction between GT and CT; each system simply passes information using different ways of coding.

This information-centered view has several consequences.
1. There are no boundaries on the types of physical entity that can carry information. This is true for
cultural and genetic forms of transmission.

The lack of a single empirical entity means that we have to define one.

Although there are no agreed-upon empirical units of CT, we can build a unit for measuring CT. For example, Pocklington and Best (1997, p. 81) define CT units as ‘‘the largest units of socially transmitted information that reliably and repeatedly withstand transmission.’’ This definition makes it clear that CT units are measurements of the effect of transmission on variability, not a physical package of something.

2. We must keep the physical package separate from the information being transmitted. We are not interested in the physical package or set of physical packages of cultural information but rather the structure, content, and ultimate effect on observable phenomena like material culture.

Because variation is the raw material upon which evolution operates and CT processes directly affect variation, it stands to reason that CT strongly affects the course of evolution.

Transmission works on both the originating (i.e., source of information) as well as the recipient
side (i.e., the destination of information) as individuals acquire, store, recall, replicate, and materialize this information.

Different combinations of the content, context, the number of people involved, the direction of transmission, biases, and information packaging present a bewildering array of possibilities for CT.

Content refers to the actual information that is being transmitted between individuals.

The more complex information is, the longer it takes to describe its properties whether done
mathematically, pictorially, or verbally. For evolutionary modeling, the complexity of information is important because complex information is subject to greater copying error.

The various human sensory systems are different in their accuracy, hence the propensity to produce error during replication of cultural information (see Eerkens 2000; Eerkens and Bettinger 2001; Eerkens and Lipo 2005).

The repetitiveness of the information being transferred also affects error rates during replication. Information that is highly repetitive is more likely to be materialized with less error than information that is singular.

The structure of information affects how it is transmitted. Mesoudi and Whiten (2004) showed that social information loses detail (‘‘low-level information’’) but may gain high-level structure as it is transmitted between people verbally. Mesoudi (Mesoudi et al. 2006) suggests that social gossip is transmitted with greater accuracy than similarly structured but nonsocial information.

Washburn (2001) found that the overall structure of the images was more accurately reproduced than elements about detail. Furthermore, cultural background played an important role in the accuracy of reproduction; the greater the familiarity of the culture from which the image was drawn, the greater the accuracy in reproducing structure and especially detail.

Context refers to the social and physical setting in which cultural information is transmitted. The physical and social context of transmission can mediate or alter the content of what is being transmitted.
The context in which this information was transmitted greatly affected variability in how it was remembered and subsequently retransmitted (Barth, 1987, 1990).
It is possible that artifacts transmitted within ritual contexts had conservative rates of change.

Mode refers to the process by which individuals transmit and acquire information.
Cavalli-Sforza and Feldman (1981) and Boyd and Richerson (1985) have modeled, that different modes of transmission can have dramatic effects on the rate of evolution of cultural information. For example, many-to-one transmission tends to slow the rate of change relative to one-to-many transmission (MacDonald 1998, p. 230; Shennan 2002).

Conformist transmission is a many-to-one system but with a particular type of bias, where the ‘‘many’’ represents those individuals possessing the modal or average behavior.

Rarity (or pro-novelty) biased transmission

Prestige-biased transmission – certain prestigious individuals, rather than the masses, are assumed to have access to (or have experimented to acquire) superior information.

The mode of transmission can vary depending on how information is packaged.

In some cases, cultural information may be transmitted because it ‘‘hitchhikes’’ with other information (O’Brien and Lyman 2003).

Individuals (i.e., actors) receive information and intentionally act upon it (e.g., ignore it, choose from whom to accept it, modify it, experiment with it).

Example: Cultural transmission in material artifacts of archeology

Style is something that exists independent of an observer, that is, it is empirical.

Meltzer (1981, p. 314) suggests that ‘‘in many instances, the choice between certain kinds of design elements on ceramics is not a functional consideration, but rather is historically determined and selectively ‘neutral,’ because there is no inherent advantage between one element and the next. The actual presence of the design, however, has a selective value because that particular design serves to mark a certain individual or group boundary (or whatever other function it may serve).’ Style is, therefore, a way of measuring and explaining material culture through the conceptual framework of cultural transmission.

Fundamentally, a random copying model is a null hypothesis.

Mithen (1997, 1998): humans link the difficult information to things they can easily transmit as part of packages.
Certain kinds of information will be bundled during transmission.
According to Mithen’s argument, the presence of such domain-specific structure within the brain results in strong patterns in covariation between certain kinds of information during cultural evolution.

Henrich (2004b) shows that the effective population size (i.e., the number of interacting social learners) is an important factor in the transmission of complex versus simple material technologies. Henrich finds that complex technologies tend to be lost when populations decrease in size while simple technologies are maintained or even improved.

Various transmission processes produce different patterns in variation, with some such as conformist transmission removing variants from the pool of behaviors (i.e., winnowing away), and others such as experimentation and innovation adding new ones.

The documentation of variability and measures of dispersion (e.g., standard deviation, coefficient of variation) and covariation are not systematically reported in archaeological research. To maximize the utility of CT, it is important that archaeologists consistently report and consider the explanatory implications of dispersion measurements as well.

As a conceptual framework, CT is especially powerful for explaining patterns observed in material culture and variation therein through time and space.
Explanations of artifacts include the aggregate of ideas and processes involved in construction and how these are transmitted between individuals while simultaneously being modified through copying error, individual learning, experimentation, or innovation (e.g., Basalla 1988).

Three measures of variation are of particular relevance to CT.

The first is the dispersion about a mode or average. Dispersion can be caused by a number of transmission processes such as purposeful experimentation or copying error.

The second measure of variation concerns the diversity of distinct types within an assemblage of artifacts.

The third measure of variation involves covariation between the attributes of an artifact or between artifact types themselves.

Few studies have explored the intersection of these three measures of variation (dispersion, diversity, and covariation).


Elaborating Connectivism framework: deepening the ecological focus

September 17, 2008

This chapter draft 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 the Lotman’s ideas from cultural semiotics.

It appeared in:

Pata, K. (2009). Revising the framework of knowledge ecologies: how activity patterns define learning spaces? In Niki Lambropoulos & Margarida Romero (Eds.), Educational Social Software for Context-Aware Learning: Collaborative Methods & Human Interaction. IGI Global imprints.


Intervention into formal elearning

September 9, 2008

Need for intervention has been discussed in one of the Edmedia 2007 symposiums.

Here i and Terje Väljataga provide the draft description of the main requirements for teaching and learning in open distributed learning environments that combine institutional tools, as well as, social software, when focusing on promoting collaboration, social networking and self-direction competences. These learning settings were planned and tested in three case studies of the iCamp project.

The iCamp project aimed at introducing interventional strategies to the formal educational environments, bringing these closer towards new frontiers in the changing work environments.

Where intervention is needed?
In formal educational settings teaching and learning is overtly conducted in the highly structured learning management systems and abstract contexts, which differ from the future work settings, where using top-down maintained systems may be impossible, and the tasks are rather situational and problem-based.

The developers of the institutional learning environments are ignoring the fact that there is a growing chasm between the choice of tools and selection of learning patterns in formal learning-settings and informal real-life settings.

The courses provide few possibilities for self-directing learning process, and do not support planning for personalized learning paths beyond institutional and formal frameworks.

Cross-border collaboration and learning in communities and social networks, using various social publishing, managing and communication tools, also combining them with institutional systems is, however, already becoming a mainstream strategy in the real-life project situations.

Areas of intervention

Based on these limitations of the formal teaching settings to meet the competences needed in post-industrial work framework, the following areas of intervention were identified:

– Planning learning in the conditions of uncertainity and ambiguity in the dynamically evolving learning environments, which presume continuous willingness to learn, and competences to manage and update the systems according to the current context.

– Accepting the need for assembling learning environments beyond institutional borders and learning management systems – integrating social software and LMS systems into various learning landscapes, following the learners’ objectives.

– Facilitating the freedom of entering into the formal learning process with different Personal Learning Environments (PLEs), promoting the willingness of using these systems consistently in formal and informal learning settings for life-long learning.

– Introducing the need for shifting the responsibility of planning and maintaining the personal and group learning environments and learning patterns from teachers towards learners in order to facilitate self-directing competences and enable learners to achieve stronger compatibility with various contexts.

– Putting the focus of some courses in higher education on planning, monitoring, and evaluating personal and group learning processes beyond the specific domain-centered courses for promoting the development and internalization of self-directing competences.

– Inviting teachers and learners to initiate and participate in the challenging real-life assignments, involving communities and networks beyond institutional counterparts.

– Changing the focus from assignments that promote private individual learning of knowledge and skills towards those that favor shared community-based knowledge building and obtaining the complex competences.

– Accepting and promoting learner-defined context and contents and initiating social publishing.

What we expect from learners?

Following these areas of intervention it was expected that in the exploratory cases learners were:

– Involved into the collaborative knowledge-building activities and getting experience of working in networked communities.

– Prompted to plan, monitor and suggest evaluation methods of their learning activities, becoming more self-directed.

– Guaranteed a freedom of choosing the most suitable tools for their learning activities, and achieving competence of maintaining their personal and group learning environments for realizing individual and group objectives.

What facilitators must consider?

The empirical explorations, how learning should be organized in this increasingly unpredictable context, where learners and facilitators are confronted with complex, dynamically changing, and unexpected requirements, were one of the major pedagogical tasks and contributions of the iCamp project.

Analysis of the exploratory iCamp cases revealed several aspects what the facilitators need to focus when entering into the new learning situations:

– Dealing with the institutional restrictions when planning for integrated courses between institutions or beyond higher institutional border: difficulties in integrating systems, planning domain tasks, integrating assessment criteria.

– Coping with the facilitators’ and learners’ stress that originates from starting learning in the initially unstructured system that is evolving in the course of action.

– Being prepared to reorganize the facilitation responsibilities beyond institutional borders: monitoring, scaffolding, providing technical help, organizing assessment, motivating students.

– Reorganizing domain learning into problem-based, integrating problems from various learners’ work contexts.

– Coping with the facilitators’ and students’ stress that initially learning environment cannot be filled with teacher-defined contents but the contents would be evolving as part of the activities and are learner-defined.

– Overcoming the suspicions related with open social publishing, dealing with copyright issues and fear about the quality of learning materials.

– Making amendments to the domain learning for the sake of developing self-directing competences in an entwined way in order to increase the possibility that the learners could use these skills later in similar contexts.

– Accepting that self-directing, if not internalized will be hindering domain learning, and coping with the subsequent stress learners would feel.

– Originating grounding events for planning the shared tool landscape and shared objectives.

– Being aware of and coping with the tension and stress that students perceive when self-directing in collaborative and social networking situations.

– Integrating individual and collaborative assignments to deal with the competition between individual self-reflection and group level reflection and activities at both levels.

Composing personal learning environments (PLEs)

An iCamp project treated a personal learning environment (PLE) concept more as subjective, psychological concept, offering a broader, naturalistic view on what comprises a personal environment in which intentional learning is carried out. It was assumed that individuals who need to select the technological means for creating personal or distributed environments in order to support their own work and study activities also need to be competent in terms of managing technology and its subcultures and common practices. Thus forming a PLE of tools and services, resources and people often requires a trial-error approach, which in turn can help to advance the necessary dispositions (knowledge, skills, orientations, etc.) for self-direction in education. However, obtaining the competence with evolving new social technologies and practices can be gained only if using these environments in various personally meaningful activities without the fear to learn from failures.

A PLE of the students and facilitators in iCamp cases entailed all the instruments, materials and human resources that an individual was aware of and had access to in the context of the educational projects at a given point in time (Fiedler & Pata, 2008). The PLEs were constructed both by the learners, as well as, by the facilitators, indicating that in iCamp intervention models the distinction between different roles of learners and teachers was intentionally diminished.

Every personal environment was different, depending on the individual’s preferences and expectations, his/her process of personal development and mental processing. Individuals constructed their environments so that its components afforded them to create the experience they desired and to act according to their purposes. A PLE was entirely “controlled” or constructed by an individual and was adapted according to the individual’s needs and current activities during each case study process. A PLE was often extended, e.g. the components of an environment were replaced or complemented with additional ones. Some components were also eliminated or temporarily excluded if they did not serve the purpose anymore.

PLEs models in three iCamp cases involved the following integrated tools: WordPress (or other blog; social bookmarking tool or Scuttle (or other); Skype or XLite VoIP tool; MSN or other instant messaging tool; Videowiki; Flickr; e-mail; feed-on-feed or other aggregator; iLogue learning contract management tool.

A distributed group environment

If an individual takes part in some collaborative work- and study activities with others, some common goals and objectives for action need to be established and maintained (Fiedler & Pata, 2008). The challenge is to bring personal expectations, experiences, roles and environments together in order to form a functional collaborative setting. In this case parts of a PLE inevitably start to show qualities of a human activity system (Engeström et al., 1999). From an observer’s perspective an individual PLE starts to overlap partly with other personal environments and a temporarily functioning distributed learning environment emerges. A distributed environment serves as long as the collaboration among these individuals is going on (Fiedler & Pata, 2008).

An iCamp practice of using social software in elearning has been moving from the establishment of initial personal learning environments (PLE) towards combining these with other people‘s PLEs in order to carry out some joint learning activities (Tammets, Väljataga & Pata, 2008). This often means changing and expanding each individual‘s PLEs, and integrating new tools, resources and people to their PLEs, while suppressing the use of others in the sake of forming a shared learning environment where all the tools can be used equally by the group members for collaborative tasks.

An iCamp project conceptualized a distributed learning environment as a group managed environment that is a mix of some parts from the individuals’ personal environments and some new components that might be needed to carry out particular collaborative tasks. A distributed environment emerges when the collaborative activities such as interaction between individuals, communication and shared activities are executed. Distributed learning environments are also dynamically changing in terms of its components, structure and extension. Changes are defined by the individuals‘ preferences, negotiation process and the nature of their collaborative activities.

Individuals ascribed various roles when using PLEs in the iCamp case settings that required collaboration and social networking. The learning environments of collaborative groups and the course were constructed integrating different learners’ and facilitators’ PLEs with the shared collaborative tools. The collaborative tools used in iCamp groups and course landscapes were: social publishing tools like, Zoho, Google.groups, wikis like XO wiki, Wikispaces, and group blogs like WordPress; synchronous group meeting tools like Flashmeeting, Skype and XLite; social networking tools like; aggregators like feed-in-feed or other similar.

Competition between PLE and collaborative areas of the distributed learning environment

In distributed environments different actions can be distinguished: conversational actions related to subject-matter issues (terminology, concepts) or related to regulative issues (distribution of work, roles, media) and productive actions in which the actual task is executed and objectives are materialized (Fiedler, Pata, 2007). Naturally both types actions are highly intertwined and actors switch rapidly from one to another. In loosely-coupled, networked work-settings, both types of actions need to be mediated by an appropriate selection of tools and services. While making decisions regarding the technological enrichment of a personal learning environment only requires a conversation with oneself (reflection), collaborative settings require the explication, negotiation and mutual acceptance of a selection of technological means in order to form a functional distributed learning environment.

These two major activities: narrative self-reflection and collaboration are performed in different subspaces of the distributed environment. Furthermore, these activities are of highly competitive nature and demand a lot of cognitive effort. In iCamp case studies students were guided towards self-reflection and self-direction activities by making use of their PLEs, while at the same time they were prompted to perform collaborative activities in distributed shared learning environments. Thus students and facilitators were challenged by the competitive nature of self-reflection done in single PLEs against the other-directed reflective activities done in distributed shared learning environments.

Using feed- and tag-technologies enables people to mash and combine their different types of reflections using them as evidence of their self-directed behavior. Learners may also mash their reflections with those of their co-workers, peers or experts they monitor, thus, creating and visualizing new challenging and maybe controversial constellations for them to ponder about. Social software enables individuals also to publicly distribute their personal reflections and to share them within groups and communities, since personal self-directed work forms the basis on which the group work builds on.

A conflictual situation with regards to self-reflecting practices and collaboration may emerge when learners are challenged to look at their personal learning activities from the group perspective. In collaborative settings self-direction has to take place in the social context of the group, personal planning and actions need to be related with the shared outcomes of the group. To maintain the learner‘s motivation, the individual learning objectives need to be entwined with those objectives people have as members of the group. This means that the personal learning contracts have to be dynamically revised in the group context. In addition to reflective self-evaluation, in group context, the peer-evaluation becomes an important criterion also for the individual learner’s progress. Self-reflection may be distracted by other-reflection practices the group performs during collaboration. Instead of looking at how the individuals achieve their learning goals as part of the group the focus shifts on reflecting how well the group and its individual members perform.

The model introduces the assignments and artifacts in three iCamp cases.

The research group within the iCamp project conducted several learning experiments in which learners were prompted to establish their PLEs and conduct self-reflection in their personal environment, while simultaneously being involved in the group activities in various types of distributed environments (eg. shared weblog, shared wiki, combined distributed learning environment from various socials software tools). In iCamp case studies students were asked to set up their personal tools landscape from a range of pre-selected tools and where assisted by their facilitators and technical support. Then they were asked to form groups of 4-6 students from different countries for certain group work. For this collaboration task the students had to negotiate their common tools landscape and thus create a distributed learning environment. For self-reflection purpose the students were asked to establish a personal learning contract and to do regular (once a week) reflective writing in their learning process. The students were recommended to use either iLogue or their personal weblogs for this self-reflection task.

Important stages in the activity patterns of interventional course design were following:

• Cross-institutional planning of the course: tools and systems, interoperability, content, problem-based project topics, facilitation, assessment

• Establishing readiness for intervention among facilitators: motivation, learning competences in ambiguous situations, dynamically evolving course environment, self-directed students, conversational scaffolding at learning contracts

• Assembling the learning environment – diversity of personal environments and group environments, learning to use the systems

• Forming the groups: topic-based selection, peer-selection, facilitator-selection

• Individual assignments: self-reflection, conversational learning contracts, personally motivating

• Collaborative assignments: domain learning, team-level regulation, grounding the learning environments, learning from the process

• Facilitation and peer-support: Monitoring and regulating, motivating, coping with stress situations

• Regulating cross-institutional facilitation: learning from facilitator experiences

• Assessment: self-evaluation, peer-evaluation, project-based-evaluation, assessment of individual- and group-work, coherent cross-institutional grading

In the three case studies of iCamp project the complexity of new elements eg. self-directing with conversational contracts and social networking, increased. This was because of learning from the previous experiments.

In the first case study, the focus was on collaboration. Although students and facilitators were instructed to initiate PLEs, the PLE use was not supported by any official course assignments. The main central collaborative space was a shared weblog, but this environment was occasionally extended with other tools for social publishing, aggregating information, communicating synchronously. The main observations from that study indicated that the students did not use their PLEs if the PLE use had not been internalized, the PLEs were neglected for the sake of visiting and working in the collaborative space (Pata & Väljataga, 2007). Another observation indicated that in case of tension in the group, the tasks were overtly conducted in one central environment of the group – in the shared weblog. However, if the group had bigger coherence in objectives, task- and role-management, they used various tools and divided the actions between the tools – for example planning was done in the Skype or Flashmeeting tool and simultaneously a summary of talked issues was composed in the environment.

In the second case study, dedicated to self-direction and collaboration, it was intended that students keep using the PLEs during the learning process actively, self-directing their collaborative work that they do in the collaborative XOwiki environment with the shared project. The usage of PLEs was supported by the task of self-reflection, using the conversational learning contract method. The students had to plan, monitor and evaluate their progress using the contract elements in their weblog, or alternatively using the special too iLogue. Facilitators, who commented self-reflections, supported the self-directed work. The collaborative work environment was a wiki for each group. Several other tools for finding and organizing relevant information and events eg. a search engine Objectspot, feed-on-feed aggregator, Scuttle social bookmarking tool and event planning tool XXX were used. The second study revealed that the students tended to neglect the collaborative wiki and work individually with the subtasks of the joint project in their PLEs. They summed up their personal work mainly by gluing parts of the individually composed artifacts together. This case study revealed that there is a competition between individual work, self-reflection and the collaborative work. The main proposition from the results of this case was the need to integrate self-reflection tasks into the group tasks and facilitate the self-reflection more actively, providing various templates and commenting students’ efforts consistently.

The third case study, dedicated to self-direction, collaboration and social networking, was run in the integrated environment consisting of the institutional Moodle for keeping learning materials, offering some central support, enabling the students to register to the course and receive the assessments in private mode. The activities of this case study took place in the distributed learning environment, consisting of various students’ and facilitator’s PLEs, where they worked individually, and of the collaborative spaces that were used for doing collaborative assignments. We provided a set of tools developed at iCamp project, but the students were not restricted to use these tools, but could find and integrate other social software tools to their PLEs and distributed group environments. It case study was considered to give students and facilitators some experience of planning and dynamically changing their distributed learning environments, which is a task of high ambiguity and involves potential tensions. It was also assumed in this case that social-networking competence would develop in the long time period, and this course can only give some experience how difficult it is to find, get connected and stay collaborating with various people.

The main finding from third study was supporting the observations from the second case about the tension and competition between individual and collaborative activities. It was found that if self-reflecting and self-directing tasks were planned as an integrated part of the collaborative activity, the self-reflection activities still tended to inhibit collaboration and other-directed reflective discourse or vice versa, especially if the students had to work under strict time limits. Our findings from the third case study indicated that it is important to relate the individual self-reflections to the group activities and create awareness about the social system in which the individual learning in embedded. New distributed social tools and services (eg. pushing feeds for the group, mashing and filtering group feeds) that enable to interact from PLE environment in the group space, would be scaffolding such learning process.

More about competition between activities

About cases:
Case 1 schemes
Case 1 group scheme
Paper of the initial design model based on case 1 data.
Case 2
initial scenarious


New about niche conceptions

September 6, 2008

Recently i have been intrigued by the niche conception and its application in the ecological learning framework common in Web 2.0 communities. Here are some papers of interest.


Macroevolution of ecosystem engineering, niche construction and diversity
Douglas H. Erwin
Trends in Ecology and Evolution Vol.23 No.6

Diversity begets diversity.

Douglas (2008) has assumed that in the macroevolutional perspective that reaches beyond species level, the niche construction, done by organisms as the feedback to their environment, and ecosystem engineering, where organisms influence the ecological success of other species, has increased over time, influencing the increase of biodiversity.

Ecosystem engineering can have positive impact on diversity by constructing habitats that can be occupied by other species, usually through increasing structural heterogeneity and patchiness and by direct impacts on resource availability.

Romer’s (1990) crucial insight was to realize that economic growth ultimately depends on the generation of non-rivalrous, non-excludable goods because these goods produce economic spillover effects (positive feedback) that percolate across the economy. These goods have a greater impact on growth than other types of innovation.

This economic insight indicates that the greatest impact of niche construction and ecosystem engineering is when they produce innovations that are analogous to non-rivalrous, non-excludable goods with long persistence (Douglas, 2008).

Author arises a number of interesting guestions that need to be answered:

How have these processes of niche generation and ecosystem engineering influenced community assembly and -recovery from mass extinctions?

Have niche generation and ecosystem engineering had a significant role in evolutionary innovations?

Are ecosystem engineers more resistant to extinction?

Has the absence of ecological inheritance inhibited recovery after biotic crises?


The niche construction paradigm in ecological time
John Vandermeer
e c o l o g i c a l m o d e l l i n g, 2 1 4 ( 2 0 0 8 ) 385–390

Niche construction: The organism has a profound effect on the very environment that generates the selective pressure to which the population of the organism responds with genetic change, thus effecting evolution.

The equilibrium theory states that there is a balance between the need for a certain population to maintain the constructed niche and the size of the population that can be sustained by that niche.

The necessary population is the number of individuals necessary to maintain a particular constructed niche.

The niche affects the organism and effectively dictates how many individuals can be sustained at a given level of constructed niche. This is the sustainable population.

The relationship between the necessary and sustainable populations defines a clear dynamic for the population and its niche.

A critical population density is necessary (along with a critical niche) for the population to be successful.

In a facultative constructive niche the organism survives even in the absence of niche construction, nevertheless benefits further from the construction.

In an obligate constructive niche the organism dies in the absence of construction.

Similarly, a facultative organism survives even in a non-constructive niche, but benefits further from the construction, whereas an obligate organism does not survive unless a constructed niche becomes available.

There also exists an evident case of niche construction that cannot really be included in the present formulation, that in which an individual manufactures some aspect of its own niche that has relatively no effect on other individuals of the population. Nests, burrows and webs, for example, are clearly examples of constructed environments, but they do not translate into population-level density dependent effects and are more akin to other individual traits such as skin color, toxicity or flight ability.

Furthermore, while niches are constructed by organisms, they are also destructed by organisms.


Revisiting Interpersonal Media Competition
The Gratification Niches of Instant Messaging, E-Mail, and the Telephone
Artemio Ramirez Jr.
John Dimmick
John Feaster
Shu-Fang Lin

Communication Research
Volume 35 Number 4
August 2008 529-547

At its most general, the theory of niche explains how media compete and coexist in limited resource environments (Dimmick, 2003).

Niche is the position of a medium in the multidimensional resource space of the environment.

The niche of a medium is derived from its pattern of resource use, represents its strategy for survival and growth, and ultimately determines its position in a multidimensional resource space.

Gratification opportunities are properties of a medium that allow users to overcome time and space constraints and, in effect, amplify or attenuate the ability to derive satisfaction from a medium.

The gratification niche of a medium is defined within a domain of gratification and gratification opportunity measures common to a set of media.

The gratification niche of a medium can be defined by its breadth on the gratification and gratification opportunities dimensions, the degree of overlap with other media, and its superiority in satisfying needs over other media within the same domain.

Three characteristics are central to understanding a medium’s niche:

1. Niche breadth, or the degree to which a medium satisfies a relatively broad or relatively narrow spectrum of media-related needs. Niche breadth can be interpreted as relative specialism or relative generalism. Specialists gratify a relatively narrow set of needs, and generalists satisfy a broader spectrum.

2. Niche overlap, or the extent to which media are perceived as similar, indicated by the distance between their gratification niches. Put differently, niche overlap is an index of the substitutability or complementarity of two media. High overlap indicates that media are substitutes or serve the same needs, whereas lower overlap indicates that different needs are being served. Thus, low overlap points toward the complementarity of the media, whereas high overlap indicates strong similarity or competition.

3. Competitive superiority, or the extent to which one or the other of a pair of media provide greater gratification. Indices of superiority for gratification measures are defined as arithmetic means, and differences between two means on a dimension can be tested for significance using a t test for correlated groups.

The complete replacement of one medium by another is termed competitive exclusion, and partial replacement is termed competitive displacement.

The mathematical measures of niche breadth, overlap, and superiority were developed by Dimmick (1993; see Dimmick, 2003, for computational formulas) as interval equivalents of the bioecological measures that are appropriate for nominal scales.

Dimmick,J. (2003). Media competition and coexistence:The theory of the niche. Mahwah, NJ:Lawrence Erlbaum.


A general framework for the statistical exploration of the ecological niche
Clement Calenge
Mathieu Basille
Journal of Theoretical Biology 252 (2008) 674– 685

The graphical exploration of the relationships between a species and its environment may rely on the formal concept of ecological niche (Hutchinson, 1957). Each environmental variable can define a dimension of a multidimensional space, namely the ecological space. In that space, the distribution of the species occurrences represents the niche, which can be compared to the environment defined as available to the species (e.g., pixels of a raster map). This concept allows both a graphical and a quantitative exploratory analysis, in order to identify the directions in the ecological space where the distribution of the species is most different from the distribution of points describing the environment available to the species.
However, the present ‘lack of effective tools for exploring, analysing, and visualizing ecological niches in many-dimensional environmental space’ (Soberon and Peterson, 2005) may render this task difficult. The Ecological-niche factor analysis (ENFA, Hirzel et al., 2002) and the Mahalanobis distances factor analysis (MADIFA, Calenge et al., 2008) are two such methods.


The neuroscience of primate intellectual evolution: natural selection and passive and intentional niche construction
Atsushi Iriki, and Osamu Sakura
Phil. Trans. R. Soc. B (2008) 363, 2229–2241

Iriki and Sakurai (2008) propose a theory of intentional niche construction as an extension of natural selection in order to reveal the evolutionary mechanisms that forged the uniquely intelligent human brain.

Tool use sets up mutual interaction between the organisms and their environments. Tools become embedded cultural traces that are used to modify the environment in which subsequent generations develop and learn. This constructed environment puts selection pressure on the species, favouring individuals with phenotypes (whether morphological features or neural circuitry) that match the usages of such traces.

People have long commented that as one becomes deft with a tool, introspectively it begins to feel as though the tool has been incorporated into one’s body image as an extended hand or forearm.

We were studying intraparietal bimodal neurons that respond both to tactile stimulation on the hand (a neuron’s tactile receptive field) and to visual stimuli presented in the same spatial vicinity as the tactile receptive field (the same neuron’s visual receptive field). These visual receptive fields were not confined to any region of the retina, but followed the hand around everywhere it was moved in the three-dimensional space.

We interpreted these neuronal response proper ties as coding the image of the hand in space ( Iriki et al. 1996; Maravita & Iriki 2004). Our next observation was surprising. When our rake-trained monkeys wielded the rake in order to retrieve food, these same neurons’ visual receptive fields extended outwards along the axis of the tool (ac) to include the rake’s head. In other words, it appeared that either the rake was being assimilated into the image of the hand or, alternatively, the image of the hand was extending to incorporate the tool.
Whenever a monkey was not regarding the rake as a tool and just held it passively as an external object (ad), the visual receptive field withdrew from the rake head and was again limited to the space around the hand.

If external objects can be reconceived as belonging to the body, it may be inevitable that the converse reconceptualization, i.e. the subject can now objectify its body parts as equivalent to external tools, becomes likewise apparent.

Thus, tool use may lead to the ability to disembody the sense of self from the literal flesh-and-blood boundaries of one’s skin.

It has been repeatedly emphasized that since changes in behaviour precede morphological changes, behaviour must be viewed as one of the prime ‘engines’ of the evolutionary process (see ar ticles in Plotokin (1988) for review), rather than simply the end product of it. Apart from some classical philosophical arguments, this kind of argument originated with Darwin (1881) himself, and has been recently re-evaluated as the ‘niche construction theory’ (Odling-Smee et al. 2003).

In all non-human species, the process of organism–environment interaction proceeds through a finite number of cycles, which eventually reaches an equilibrium point and then stops. Such interaction is purely passive, a ratchet process prefigured by the combined characteristics of the subject and the environment to which it must adapt. Thus, we can call this process passive niche construction.

When organisms become aware of ‘subjective self ’, gained ability to explicitly imitate (Iriki 2006) or intentionally plan for the future, an additional factor was added on top of a pre-existing stable mode of environment, a novel mode of evolutionary circulation was initiated by succession of sequential niche construction processes.

Taking into consideration the similarities, equivalences and differences between enhanced monkey and modern human intellectual brain functions, we proposed a novel evolutionary mechanism, intentional niche construction, which we think is necessary, in addition to the mechanisms of Darwinian natural selection and passive niche construction, conceptually proposed by Darwin (1881) and later formalized by Odling-Smee et al. (2003), to account for the full course of human intellectual evolution.

Once goal-directed intentional niche construction was introduced into the evolutionary process, biological and cultural processes became intertwined to an unprecedented degree.

Humanity faces the unprecedented situation in which numerous minds possess external thinking devices linked simultaneously via the Internet. In such a situation, might the will of individual ‘subjects’ become separate from their bodies and act mutually, through the interdependent functioning of the Internet, with the shards of a thousand selves forming the community of an imaginary society? In such an event, perhaps the advanced, virtual concept of ‘multi-selves’ will emerge, evolving through the neuro-biological mechanisms depicted here as they carry us into the future.


Return of the niche
Mathew A. Leibold
NATURE Vol 454, 3 July 2008

Traditional explanations for the local co-existence of species hold that the balance of nature is delicately related to differences in how species interact with their local environments (their ‘niches’), with populations of each species being primarily regulated by distinct environmental factors. Such niche partitioning results in stable frequency dependence, in which each species increases relative to others when it is rare, and decreases when it is common.

This venerable view has been confronted with the contention, arising from recent modelling work, that stochastic demography and dispersal are more important, and that they allow the widespread coexistence of species with identical niches. This ‘neutral theory’ has provided possible explanations for the occurrence of highly diverse communities that challenge the traditional view.