Archive for May, 2012


Learning and knowledge-building in extended organizations

May 20, 2012

The IntelLEO project is going to its end, and we are looking back at what we achieved.

In the project we assumed that to support organizational responsiveness, cross-organizational learning and knowledge-building should be supported. Our assumption was that learning and knowledge-building (LKB) activities across organizational borders as well as within organizations would create conditions for organizational responsiveness to appear.

We adopted the knowledge conversion model by Nonaka & Takeuchi (1995) into cross-organizational settings,

identified learning and knowledge-building enablers and inhibitors,

and developed technological services that support those learning and knowledge-building activities that support responsive organization to emerge:

  • the competence-based reflections in the format of the construction of learning paths and monitoring personal development in socially and organizationally embedded context (externalization and internalization activities) (see Siadty et al. 2011)
  • the competence-annotated sharing and searching of knowledge (externalization, internalization)
  • the competence-annotated searching for other learners or working partners and team-building (socialization and combination activities)
  • the construction, accumulation and provision of organizational knowledge to its employees using the semantic web technologies and ontology framework (externalization and internalization activities)

We assumed that extended organizations are connected with temporal learning and knowledge building activities, and we may conceptualize such an extended organization (an IntelLEO – intelligent learning extended organization) as a distributed cognitive system.

Socially distributed cognition, where cognitive processes are distributed across members of a social group by knowledge exchanges also contains mutual awareness, communicating and socially provided support as an external locus of control for cognition. The forms of socially distributed cognition are:

– monitoring peers’ activities for mutual awareness, social surveyillance (such as friend-feeds, wall, mashups)

– peer-scaffolding (commenting, rating, favouriting)


Distributed cognition involves coordination between internal and external (material and environmental) structures through causal coupling (an embodied cognition) that enables adapting one’s actions to fit to environmental conditions.

These also associate with the distributed intelligence and dispersed learning processes carried out in a loosely coupled way. Such distributed intelligence creates a distributed cognitive system that also contains a feedback loop to community/organizational culture – cognitive processes can be distributed through time in such a way that products of earlier events (of the same person, of other community/organization members or members from different community) can transform the nature of later events.

This may take different formats:

– creating and using personal knowledge aggregations

– using the external knowledge organization of peer’s (tags, annotations to the resources they have used)

– using bottom-up or top-down aggregated organizational knowledge (tagclouds, semantic search)

– creating and organizing personal reflections (blog posts)

– using externalized peer’s knowledge (blog posts)

– creating personal networks (mashing feeds to monitor)

– benefitting from community browsing (from shortcuts the personal networks create in the community).

Here are some results from the interviews with workplace learners about using the IntelLEO framework for learning and knowledge-building (LKB):

The temporal LKB activities that have been identified as the prerequisites of organizational responsiveness These acts create distributed cognition possibilities across EO in IntelLEO Examples of temporal LKB acts perceived by workplace learners

1. The presence of knowledge exchanges among employees

Cognitive processes are distributed across the members of a social group (a socially distributed cognition).

Better communication

Becoming open

Exchanging knowledge and experiences

Acknowledging that someone might read and learn from my reflections.

Sharing, asking and commenting to support the development of learning partners

Helping my colleagues to discover interesting online resources

Cross-organizational  collaboration on research projects

Starting and sharing new learning areas in the company

Sharing relevant information with a group

Shared goal or experience supports LKB

Sharing information complements each other’s knowledge and increases group synergy

2. The opportunity for employees within an organisation to use knowledge to adapt their actions to appropriately fit environmental conditions

Cognitive processes involve coordination between internal and external (material or environmental) structure through causal coupling (an embodied cognition)

The continuously changing and evolving job requirements impose the need for constant learning of new things

Autonomy  for deciding when and how to learn

Performing LKB primarily for oneself

Organising learner’s current/planned knowledge is increasing the willingness to get involved in LKB

Giving the big picture – what have you done, how have you done it and what else you should learn

Reflection makes to analyse development and think thoroughly about the activities

Showing the learning progress motivates others’ learning

Documenting one’s knowledge increases the others’ motivation to learn within the organization

Reading colleagues’ entries  help to realize that my contribution can also be useful for my colleagues

Seeking external solutions for internal challenges

Seeing what and how others have learnt  – that makes to think should I learn it as well, how could I learn it

Reusing the „lessons learned“ of my colleagues for planning learning

Peers’ contributions influence to see own things from different viewpoint

Providing the organisational goals on what to learn

Can take a look at the example-learning paths, created by organization

Benefiting and learning from the crowd-sourced knowledge and annotations gathered by the entire organization

Organizational goals may be harmonized with input from personal goals and work-practces

3. Distributed intelligence and dispersed learning processes carried out within loosely coupled different organisations

Processes are distributed through time in such a way that the products of earlier events can transform the nature of later events (feedback loop to organizational culture).

The sufficient mass of initial content in the system increases motivation to add

Looking back/finding at own entries and annotated resources

Identifying potential learning and/or research partners

Getting an insight into others’ interests and goals

Following resources or persons

Associating the discovered resources with the task

Letting others to know of new contributions

Seeing the activities in interesting topics and of colleagues

Better structuring and organizing of the collective knowledge

The collaborators can easily access task-relevant resources

Collaboration between organizations influences positively the development of individuals

It influences the growth of the organizational and individual knowledge


modelling open education learning ecosystem

May 8, 2012

I have tried to put together ideas associated what may be conceptualized as a open education learning ecosystem.

My aim is to propose the meta-design framework for open learning ecosystems such as open courses in distributed social software environments (see Tammets, Väljataga & Pata, 2008; Pata, 2009a,b; Väljataga & Laanpere, 2010; Pata & Merisalo, 2010) or massive open online courses (MOOC) (see Kop & Fournier, 2010; Kop, 2011).

The characteristics of open education courses

A variety of open education approaches exists, since it is a new and rapidly developing domain of elearning. However, we mainly consider such courses where course environment appears as a distributed cognitive system (Hollan, Hutchis and Kirch, 2000) of autonomous and self-directed learners. Hollan and associates (2000) explain interactions and the coordination of activities between people and technologies in whole environments assuming that people form a tightly coupled system with their environments, and the latter serves as one’s partner or cognitive ally in the struggle to control activity.

Learners in open education courses are assumed to be autonomous and self-directed and pursuing their personal goals (Väljataga & Laanpere, 2010; Pata & Merisalo, 2010; Kop, 2011). Some learners participate at the courses from outside of the control of the educational institution, whereas others follow some curriculum (Kop, 2011). The users of open learning courses may have different roles such as learner, facilitator/teacher, curriculum-coordinator, course-organizer (university), and they have different type of intentions. These user-roles need to be aware of each others’ conceptualizations of this learning system and considering it in their system application. Learners must find harmony between their own challenges and the course goals. This also immerses additional new goals to the open learning course and shifts the initial course goals. However, in these dynamically changing conditions it may be difficult to sustain the curriculum goals, organize objective assessment and meet the university requirements to objective outcomes-based education.

An open course takes place in a digital (but also a hybrid) distributed learning environment (see Fiedler & Pata, 2009; Pata, 2009; 2010; Kop, 2011), which is co-constructed by learners and teachers considering open education requirements as enablers and the curriculum goals and institutional requirements and existing institutional systems as constraints. The learners create personal learning networks (PLN) with other individuals, using social software for connecting people and artifacts. This brings variability of tools and approaches to the course, making the learning environment complex and dynamically changing (Pata, 2009). From the learner’s point of view the emerging infrastructure is a temporally extended personal learning environment (PLE), which allows sharing learning resources (people, artifacts, practices) openly among the course community/course network. It serves as their distributed cognitive system with partially external and uncontrollable locus of control. To monitor learning, learners and facilitators should be easily navigating across the course system. They should adapt themselves to the other individuals’ useful activity preferences with different PLE-configurations, especially in collaborative tasks  (Pata, 2009a,b; Fiedler & Pata, 2009). From the teacher’s side, an awareness of the whole system affordances as they are perceived by these learners is needed (Fiedler & Pata, 2009), and communicating this back to learners would serve as a powerful scaffolding element (Pata, 2009b). This requires new type of learner-friendly accumulative learning analytics to appear that visualizes which affordances of the emerging learning system prevail and are effective in certain periods for all the learners. Open education paradigm also applies to sharing course designs and teaching ideas among teachers.

To summarize, the individuals’ self-directed learning behavior, personal learning environment (PLE) and -network (PLN) creation, and open publishing and sharing cause this course to be open, dynamic and evolving system. In one hand, learners have to adapt themselves to the course systems and goals. On the other hand, they constantly modify both. The facilitators must handle this intentional and technological freedom that the self-directed learners bring to the course. From the curriculum and system administration side these two aspects are important as well: a) curriculum requirements and technological constraints shape teaching and learning activities at the course, and b) learners’ and teachers’ actions transform environmental constraints into organizational structure and shape it (Bailey & Barley, 2011).

Why ecological metadesign is needed?

On one hand, the emergent and complex nature of open learning environments suggests embedding ecology principles into the learning design (Young, 2004; Kirchner et al., 2004; Frielick, 2004; McCalla, 2004, Lukin, 2008; Pata, 2009a,b; Pata, 2011; Reyna, 2011). On the other hand, learners who try to adapt themselves to the course environment and simultaneously modify it will make the course environment complex, dynamic and difficult to manage, and some means of regaining the co-control are needed for the facilitators, course developers, organizations and administration. Therefore, the ecological meta-design framework for open learning ecosystems was developed, that considers ecological approaches to cognition (e.g. Gibson, 1976; Varela, Thompson & Rosch, 1991; Bardone, 2011), builds on ecological principles applicable in digital ecosystems (see Pata, 2009a,b; Whelan, 2010; Briscoe, Sadedin & DeWilde, 2011), and specifically highlights the need for using the meta-design for designing the design process for cultures of participation (Fischer, Giaccardi, Ye, Sutcliffe & Mehandjiev, 2004) for supporting self-directed learners with social software in open course communities.

Open education ecosystem habitats

How ecology concepts and principles could be transferred to open education domain?

Davenport and Prusack (1997, p. 11) primarily used the information ecology as a metaphorical term to capture holistic and human-centred management of information. Later several researchers have assumed that ecology principles may be transferred to describe the social, management, design and learning aspects in human computer-supported knowledge networks (Pór & Malloy, 2000; Pór & Spivak, 2000; Siemens, 2006), digital systems (Benyus, 2002; Boley & Chang, 2007; Whelan, 2010; Briscoe, Sadedin & DeWilde, 2011) and digital learning systems (Frielick, 2004; McCalla, 2004, Lukin, 2008; Pata, 2009a,b; Pata, 2011; Reyna, 2011). We  assume in this paper that it is feasible using the ecology concepts and principles directly for developing our pedagogical framework for designing new type of learning courses, because these principles enable to see the components and processes in learning environment from the evolving system viewpoint and also suggest effective approaches for the system maintenance.

As a starting point we outline the ecological principles, useful in our framework. Ecology as a discipline deals with different levels of structural elements of ecosystems, both biotic and abiotic. Biotic factors are organized in a set of entities grouped in a growing complexity order: individual organism, species, populations, communities and ecosystem. Note that there is no consistency in literature what to consider as biotic and abiotic factors in digital learning ecosystems – users, content, technology and services have all been classified as “living” species or as part of the abiotic environment (see McCalla, 2004, Fischeman & de Deus-Lopez, 2008; Chang & Guetl, 2008; Uden & Damiani, 2007; Lukin, 2008; Pata, 2009a,b; Pata, 2011; Reyna, 2011). In the following paragraphs we describe how we relate the ecology concepts with open learning ecosystems.

The branches of ecology as a discipline deal with different complexity levels: Behavioral ecology focuses on the individual organisms of the species with variable phenotypes and behavior. Individual organisms have awareness and they interact, communicate, move, reproduce, and die while living in certain conditions and surroundings. Depending on these interactions the fitness – the extent to which an organism is adapted to cope in the particular environment – is determined. Individual organisms operate for their own benefit or profit and have intentionality. They compete with other organisms from this or from other species for limited resources. In our framework we consider the digital services (e.g. learning services – such as provision of scaffolding and learning contents, technological services – OER services such as Creative commons etc.), digital technology (such as authoring environments (blog, microblog), mashup-environments (aggregator), social repositories (delicious, youtube), and digital contents (such as blog-posts, comments, ratings) which have been actualized in particular persons’ view of the course environment as the alive “digital specimen” of the certain “digital species”. (Note that besides the learner role, we also see course the facilitator’s and curriculum/system administrator’s roles as users.) Intentions of individual users, as well as their community-cultural belonging give the variability to the “specimen” within “species”. For example the open education culture may influence the intentions of the learner, facilitator or the curriculum/system-administrator in the learning environment*.

Population ecology deals with populations of organisms of species, and studies the variability, the abundance and the distribution of individual organisms within one population and within one species in certain habitats. Species is an abstraction for the class of organisms, having some common qualities, characteristics and behavior and ability to give offspring. Species exists in time as a range of qualities, characteristics and behaviors inherited or learned from those individual organisms that were fit to the living conditions. Each species uses a particular niche – this concept denotes the abstract range of biotic and abiotic conditions that enable the fitness of the organisms of this species. A “digital species’ niche” may be conceptualized as an abstract range of dimensions that specify what the environment affords for the particular “digital-species”. Note that not each activated “digital specimen” of certain “digital species” in the open course environment may be totally fit to the “digital species niche”. For example: the niche dimensions for different types of Creative commons licenses may be attributing/sharing/derivating/commercializing, but the fitness of certain user-activated license in a course depends on if the course provides those dimensions so that using the particular license would be effective. An important ecological principle is that for any “digital species” the “niche (as a range of specific affordances)” is determined by those “digital specimen” that were activated by users. “Specimen of the digital species” adapt to the “digital species’ niche”, but also create and modify this niche and the conceptualization, what they are as a “digital species”. We discuss this feedback-loop in more detail in chapter 2.1.

The community ecology focuses on the coexisting communities of species (note that the concept is different from what is a community of people), their composition, interactions, organization and succession, as well as, on the web of energy and matter among species. A community is a temporary coalition of naturally occurring group of populations from different species that live together in the same habitat interacting with each other and with the environment. Important characteristics are the diversity of species within the community, their connectedness and aggregation, the competition between species for resources, the mutualisms that are associated with energy and matter exchanges (including parasitism or symbiosis) and communicative interactions between species. Competition has been viewed as one of the strongest and most pervasive forces in community ecology, responsible for the evolution of many characteristics of organisms. Natural selection, and hence the evolutionary process, are the outcome of competition; and are governed by density and diversity of species in the community.

While niche is an abstract conceptualization what the species would need for fitness, a habitat is a distinct part of the real environment, a place where an organism or a biological population normally lives or occurs and can be most likely to be found. The diversity of different habitats may be classified under biotopes – areas that are uniform in environmental conditions and in their distribution of species under communities. The biotope concept integrates the environmental factors, which structure the habitat – geographic locations, abiotic features and ‘modifiers’, and species. The biotopes are named after the dominant and structuring biological elements; hence their description does not need to contain ALL species in a community. Biotope offers certain abiotic/biotic factors that influence the wellbeing of the populations of several species (communities).

The concepts biotope and ecosystems differ from each other – the former is used for classification purposes, the latter in case of explaining trophic relations. Within each ecosystem are different biotopes (such as among the semi-natural ecosystems are lawns, wastelands, streets, pavement cracks and walls/roofs, however in different geographical regions we may find there certain communities and other abiotic features that give the names for specific biotopes, e. g. the lawns in coastal area, in the parks). For example, we may consider a certain open education course as a “biotope” type. MOOC, Wikiversity-course or courses held in using the combination of LMS system and distributed social software are kind of biotopes, but all together these may be conceptualized as the “open education ecosystem”. One biotope may be co-occupied by different species and be their habitat, because the niches of these species differ and can overlap. For example the open learning course as a biotope may be inhabited by different “populations of digital species” activated as parts of learners different PLEs (e.g. some may use WordPress, some Blogger; some may prefer filtering by specific content-tags, others monitoring by person-feeds).

It is important to note that in natural biotopes the resources are limited by abiotic components and biotic components only compose, exchange, accumulate and decompose carbon, nitrogen and other important elements within the web of energy and matter. Several authors have conceptualized “teaching and learning” as this energy that fuels learning ecosystems and transforms the matter “information” to “knowledge” (Frielick, 2004; Reyna, 2011). Differently from these authors, we intend to use the attention of users as one of the analogues of energy in digital open education ecosystems. In open course biotopes the limited resources may influence the fitness of “digital species” as well – for example if there are not sufficient learners and teachers who prefer certain services (such as tagging, friendfeeds), the other users cannot not benefit from community browsing as a knowledge-building strategy. Connectivity of biotopes is important as well, since organisms usually move between suitable habitats in different locations. For example at the “open learning ecosystem”, the same “digital species” (e.g. OER services for openness such as Creative commons) reappears as the license of digital contents at “digital biotopes” like MOOC, OER Index etc. This would allow the users of the digital species to freely move between these suitable habitats, using OER at constructing MOOC for example. When the density of certain habitats within an ecosystem falls below a critical threshold, widespread species may fragment into isolated populations.

The ecosystem ecology deals with the trophic relations – the energy and matter flow in ecosystem. The ecosystem concept considers animals and plants in groups, together with the physical factors of their environment, as a fundamental ecological system. An ecosystem consists of all the organisms living in a particular area (biotic component), as well as all the nonliving, physical components of the environment with which the organisms interact, such as air, soil, water, and sunlight (abiotic component). The transformations of matter and energy are mediated through the functions and behaviors of living organisms and abiotic components. Individuals within one species and between the species interact with each other and the implications of these interactions impact on the energy flow. The permeability of a natural ecosystem to the export and/or import of energy and materials will depend on the nature of the ‘architecture’ of the components of the system, and characteristics of individual species within in the biological component. We define “open digital learning ecoystem” as an adaptive socio-technical system consisting of mutually interacting proactive and responsive regarding to their own benefit/profit digital species (tools, services, content used in learning process) activated by communities of users (learners, facilitators, experts) within their social, economical and cultural environment. In our approach the user-activated “digital species” have variety of connections between them where they use available information(=matter) and transform it to knowledge(=matter) using the attention(=energy). Communicative interactions such as requesting, informing and sharing information and knowledge (Tommasello, 2008) may be direct or the ”digital species” may use information temporarily offloaded/flowing in the system (to other services, to digital contents or software).

Interactions between species also result in an open, loosely coupled self-organised and emergent ecosystem to appear. Self-organisation is a process through which the internal organisation of an open system increases in complexity without being guided or managed by an outside source. Characteristics that promote self-organisation include positive feedback, negative feedback, a balance of exploitation and exploration, and multiple interactions.

*Do we consider users as species or not? Autotrophes can turn energy to energetic products: information to some meanings in case of contents; activate different tecnology species potentials by evoking affordances? We could consider services, software and content as heterotrophes? They need to use energy-rich products.

The three big principles that may be used from ecology in open digital learning ecosystems are:

The first important assumption in ecology is that the flow of energy and the exchange of matter through open ecosystem regulated by the interactions of species and the abiotic component (by the web of energy and matter). Frielick (2004) and Reyna (2011) conceptualized “teaching and learning” as this energy that empowers digital learning ecosystems to changing “information to knowledge”.  The permeability of a digital learning ecosystem to the export and/or import of information and knowledge depend on the nature of the ‘architecture’ of the components of the system (e. g. connectivity, clustering), the characteristics of species, and their diversity and distribution, and interactions between them (such as commensalism).

Second important ecological principle is the feedback loop to and from the environment (dynamic contexts) that enables species to be adaptive to the environment and the environment to change as a result of species. A recent literature in evolutionary theory provides the idea of niche construction (Odling-Smee et al., 2003) as an ecological factor that enables organisms to contribute for and benefit from environmental information. They argue that the organisms have 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 niche construction 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. Ecological inheritance is a modified environment influenced by organisms, their ancestors or other organism communities what has evolutionary effect and selection pressure to organisms. 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.

At each level of the ecosystem dynamic agents maintain fitness with one another and within dynamic contexts. This does not happen at the species level. The proactive agents in natural systems are the self-directed specimen from the species who have variability in phenotype and behaviour that influences their fitness to the environmental conditions. At the species-level the fitness of individuals to the environment defines the range of the niche that is suitable for this species for living. Niche is an abstract conceptualization and denotes the range of conditions to which the specific species is best fit of. Hutchinson (1957) defined niche as a region (n-dimensional hypervolume) in a multi-dimensional space of environmental factors that affect the welfare of a species. In our approach the “service-species” are activated by users with different roles (learner, facilitator) and their learning intentions. Ecological psychology (see Gibson, 1977; Young, 2004) suggests that learner’s/teachers’ perception of the learning environment action potentialities (affordances) varies and this would give the variability to the actual use of services in the e-learning system. The niches for each service-species in the digital ecosystem may be collected from this user-behaviour, for example by learning analytics. Individual users activate specimen of the services as part of their learning environments in one hand, and at the same time they are influenced by the niche that each service takes due to many users’ actions. Young (2004) has written that following ecological psychology principles learning is the education of intention and attention, where motivation is reinterpreted as an on-going momentary personal assessment of the match between the adopted goals for this occasion and the affordances of the environment.

III. The third important principle that we consider from ecology is associated with the communicative interactions between species. The digital community is a naturally occurring group of “service-species” populations in e-learning ecosystem who inhabit the same habitat (but use different niches) and form temporary coalitions (communities). For example the mutualisms such as parasitism, symbiosis may appear between service species are associated with sharing the resources and associate with our first principle (energy and matter exchanges in the network).  Other type of interactions, based on communication, which assumes mutual awareness, signaling between agents (or using the accumulated signals left into the environment) may be distinguished as well.

As a result of applying these three ecological principles an open, loosely coupled self-organised and emergent digital learning ecosystem can appear.

Ecological principles


I. The flow of energy and the exchange of matter through open ecosystem regulated by the interactions of species and the abiotic component (by the web of energy and matter).Odling-Smee at al. (2003) called it the “currency” of the ecosystem.*

The permeability of a digital learning ecosystem to the export and/or import of information and knowledge due to teaching and learning power that it has.

a) The characteristics of service-species that influence to changing “information to knowledge” (their diversity).

The ecological expression of semantic information by niche constructing organisms is what grants ecosystems much of their impressive structural and functional complexity.*

b) The connectedness/ aggregation of the services (e. g. connectivity, clustering, and distribution) influences network’s ability to change “information to knowledge”.

c) The mutualisms (such as parasitism, symbiosis) that may appear between service species that are associated with energy and matter exchanges in the network

d) Competition has been viewed as one of the strongest and most pervasive forces in community ecology, responsible for the evolution of many characteristics of organisms.

II. The feedback loop to and from the environment (dynamic contexts) that enables species to be adaptive to the environment and the environment to change as a result of species’ action

a) The proactive agents in natural systems are the self-directed specimen from the species (service activations by users, what affordances are invoked), the fitness of specimen to the environment defines the range of the niche that is suitable for this species for living – the feedback from environment to the specimen of species (aggregations, visualizations of accumulated info)

b) The niches for each service-species in the digital ecosystem may be collected/accumulated/ aggregated from this user-behaviour, for example by learning analytics – the feedback from organisms to the environment must

c) Species evolve in response to selection pressures modified by themselves and their ancestors – the feedback from organisms to the environment must persist for long enough, and with enough local consistency, to be able to have an evolutionary effect.

d) Environment can have a reciprocal effect on certain species but also on other species

III. The communicative interactions between species.

a) The mutual awareness between specimen of the species and between species (dynamic awareness such as signaling between agents)

b) The mediated awareness (such as using the accumulated signals left into the environment)

* added from comments i received from Emanuele Bardone

Eventually, at different succession levels of biotopes different species appear and the application of ecological principles appears with certain variations. I have tried to model some types of open education courses biotopes and the aspects that influence “species” in these.

*as offspring we may look other actualizes species by humans


thinking of the nature of ecology of mind

May 2, 2012

I have started some collaboration with Emanuele Bardone on defining ecology of mind.

Here are some initial thoughts and examples how to see ecology of mind in spatial terms.

There are some approaches that support cultural inheritance, ecological inheritance mechanisms, such as:

  • engineering web
Nontrophic and indirect interactions between species—that is, by the engineering web (Jones et al. 1994).
Jones, C. G., Lawton, G. H., & Shachak, M. (1994). Organisms as ecosystem engineers. Oikos, 69, 373–386.
Jones, C. G., Lawton, G. H., & Shachak, M. (1997). Ecosystem engineering by organisms: Why semantics
matters. Trends in Ecology & Evolution, 12, 275.
  • external memory field
External memory field is essentially a cognitive workspace external to biological memory (p.296-297)
The external memory field is a temporary arrangement of some of the material in external symbolic storage system, for the use of one person (p.306)
Individuals connected to cultural network can access and exterior memory bank, read its codes and contents, store new contributions in permanent form, and interact with other individuals who employ the same codes and access routes (p. 311-312).
Human minds float freely, without any apparent physical tie-in, either temporary or permanent, to cultural devices (p.312).
The brain may not have changed recently in its genetic makeup, but its link to an accumulating external memory networks affords it cognitive powers that would not have been possible in isolation. (p.312)
Each time when brain carries out an operation in concert with the external symbolic storage system, it becomes part of the network. Its memory structure is temporarily altered, and the locus of cognitive control changes. (p. 312)
we are permanently wedded to our great invention (…external memory…) in a cognitive symbiosis unique in nature (p.356)
…within the context of hybrid mental architecture...consciousness can take many forms..(p.368)
…in case of television, the viewer yealds control to the external system, the screen becoming the external memory field (p. 372)
Merlin Donald (1991). Origins of the Modern Mind.
  • cultural and ecological inheritance
Natural selection and cultural selection are both involved when the animal population is human.
NCT (niche construction theory) is sometimes referred to as “triple-inheritance theory” (genetic, cultural, and ecological inheritance; e.g., Odling-Smee et al. 1996, 2003; Laland et al. 1999, 2000, 2001; Day et al. 2003; Shennan 2006).
The niche-construction perspective stresses two legacies that organisms inherit from their ancestors, genes and a modified environment with its associated selection pressures. 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 (Odling-Smee et al., 2003).

Odling-Smee, F.J., Laland, K.N., & Feldman, M.W. (2003). Niche Construction: The Neglected Process in Evolution. Monographs in Population Biology, 37, Princeton University Press.

  • ecological knowledge
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, H. (2001). Ecological psychology in context. : James Gibson, Roger Baker, and the legacy of William James’s radical empiricism. Lawrence Erlbaum Associates, Publishers.
It also associates with the cultural interface  concept (Manovich, 2008).
  • cultural interface
Lev Manovich in Software takes control (2008), uses cultural interface concept to describe human-computer culture
The interface is habitually the crucial boundary, or zone of articulation and translation whenever a computer would communicate with technological devices or the human user
“Cultural interfaces”, not just the diverse software interfaces of new media but also the formal traits and user practices with the printed word and cinema, can migrate into, and become part of, the interfaces of new media.
“While operations [like selection] are embedded in software, they are not tied to it. They are employed no only within the computer but also in the social world outside it. They are not only ways of working within the computer but also in the social world outside it. They are… general ways of working, ways of thinking, ways of existing the computer age.”
  • distributed cognitive system
I had to analyze our IntelLEO project results (cross-organizational leaning and knowledge-building supported by technological services), and as we have considered that it may be seen as a distributed cognitive system.

Then i was thinking of the ways how locus of cognitive control was partially external:

– the system services support
– the offloaded contents (of the person, of other persons, from organizations/cultures), meta-structures of content that reveal big picture or crowd knowledge (aggregations such as tags, tagclouds, ontologies) or meta-level increased access to certain contents (mashups, networks)
– the social support (such as awareness, social(participatory) surveillance, peer-scaffolding by commenting, rating)
These associate with the three ecological principles as well.

support web concept for teacher’s learning environment

May 2, 2012

Recently i was supervising the master thesis of Kristi Laanemäe. She conducted the formative analysis (interviews with art teachers who had used the support-web) to develop and validate and improve the support web concept with social media for art education set “Ready! Set! Art!” website.

The goal of RSA support web is to:

  • make RSA art educational materials dynamic and constantly improved
  • give additional value to RSA art educational materials
  • use as few resources (money and time) as possible to update and develop RSA support web
  • distribute and advertise RSA art learning environment and NGO ideas and image
  • bring art education up to date.

Improved support web:

  • accumulates access to all the different parts of support web
  • collects and accumulates artifacts automatically
  • gathers produsers
  • distributes automatically or enables to manually distribute artifacts (student works, feedback and additional materials)
  • enables web based communication
  • guarantees user-to-user feedback and organized feedback (user-to-NGO communication through feedback form)
  • provides extra value to RSA art educational materials
  • distributes and advertises RSA learning environment automatically
  • allows to manage or sort content by users needs
  • allows combined and interactive learning
  • has dynamical adaptiveness and easily perceivable structure that applies to user needs
  • motivates produsers automatically and manually.

I particularly like the figure that Kristi developed for support web concept.

Support web conceptual model developed by Kristi Laanemäe (2012) for support web of teachers' educational materials in Art.