Archive for the ‘ecology’ Category


Smartness and innovativeness of learning ecosystems

November 9, 2016

Last ICWL 2016 conference in Rome made me reconsider the innovative learning ecosystem concept in my studies and instead consider using the smartness of learning ecosystems since innovative is a relative concept while smartness is not, as well as smartness may be nicely interpreted as a niche providing fitness and flow experiences.

I liked an interesting keynote by Carlo Giovanella from Tor Vergata University of Rome – Dept. of Educational Science and Technologies. He described a survey done in several universities to capture the smartness of educational learning ecosystems – Smartness of learning ecosystems and its bottom-up emergence in six european campuses (2016): Survey with university students at different campuses: a) the detection of the degree of satisfaction related to the levels of the Maslow’s Pyramid of needs, and b) the detection of indicators related with the achievement of the state of “flow” by the actors involved in the learning processes. Identifing: a) the set of the most relevant indicators; b) a “smartness” axis in the plan of the first two principal components derived by applying a Principal Component Analysis (PCA) to the spaces of the selected indicators.

He refers to smartness as follows:

The smartness or attractiveness of an ecosystem does not depend exclusively on its ability to run “all gears” in an effective and efficient manner. It, rather, depends on its ability to create an environment able to meet the individuals’ basic needs and keep them in a state of positive tension in which their skills are stimulated by adequate challenges, to favor the achievement of the self-realization (Giovanella, 2014)  –

Giovannella C., Smart Territory Analytics: toward a shared vision. In: SIS 2014, CUEC, (2014).

NOTE: that actually is the definition of the niche in ecology, but Giovanella in 2016 article combines the Flow state as the required quality of satisfaction for people in this learning nichestate where challenges are exciting and adequate to the skills owned by the individuals, which, in turn, are expected to be improved due to the challenges.

In his previous paper of smart cities Giovanella defines smartness of cities as follows: a city is smart “when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance“.

This captures the systemic, organizational view to smartness and incorporates implicitly bottom-up self-organization in an ecosystem, and explicitly sustainability of the learning ecosystem as a common good and high quality of individual’s life as the evaluation criteria.


Giovanella’s approach technically was very similar what we have done in studying the school learning ecosystem services in Georgian, Ghanan schools (see below). However, we used observation and interviews (the external view to the existing niches).  We mapped data on the digital service grid quantitatively as an input. So we yet cannot measure the ecosystem fit to user’s challenges as the quality of smartness but rather we may set learning type variables such as learning and facilitation services related with classical ICT teaching or innovative ICT teaching and see how the other ecosystem services determine those.


Georgian papers:

Jeladze, Eka; Pata, Kai (2016). Digitally Enhanced Schools and Service-based Learning Ecosystem. EDULEARN16 Proceedings: 8th Annual International Conference on Education and New Learning Technologies. Barcelona (Spain), 4-6th July, 2016. IATED, 1569−1578.

K-means cluster analysis was run and 2 models of schools were identified using developed instrument. Discriminant analysis was run to identify predictor variables for further analysis of the schools’ belonging to certain model. Innovative and non-innovative schools differed by teacher-student partnership, authentic and flexible learning environment, but the biggest difference was in change management domain.Discriminant analysis detected following variables as predictors: school’s ICT vision and agenda, motivation and support system promoting innovative practices, teachers’ professional learning relevance to the curriculum requirements and school strategy.

Eka Jeladze and Kai Pata (2016). Technology Investment and Transformation Efforts in the Public Schools of Georgia (2016) ICWL 2016

Beyond the previous study we built Bayesian Dependency model for innovative schools’ cluster to find probabilistic dependencies of the services in digitally enhanced schools illustrated the model with qualitative case study descriptions. The findings suggested that trade-off type of services requiring schools initiative to get service and change management services were the biggest determinants of the schools belonging to the innovative technology-enhanced learning ecosystem type.

Ghanan papers:

Quaicoe, James Sunney; Kai, Pata; Jeladze, Eka (2016). Digital Learning Ecosystem Services and Educational Change in Ghana’s Basic Schools. EDULEARN 16 : 8th Annual International Conference on Education and New Learning Technologies. Barcelona(Spain) 4th to 6th July 2016. Ed. L. Gómez Chova, A. López Martinez, & I. Candel Torres. iated, 4887−4895. (EDULEARN 16 Proceedings).

This paper mapped descriptively Internal, External and Transactional Infrastructure, Learning and teaching and Change management services in Ghana and revealed the developed and undeveloped service areas for Ghanan schools and the mismatch between externally provided and internally applied services.

Quaicoe,James Sunney; Pata, Kai (2016). Digital Divide in Learning Services in Ghana’s Basic School. Advances in Web-Based Learning – ICWL 2016: International Conference on Web-based Learning – ICWL 2016 in Rome, Italy, 26-29 October 2016.. Ed. M. Spaniol, M. Temperini, D.K.W. Chiu, I. Marenzi, U. Nanni. Spring: Springer International Publishing, 83−88.

The results of Canonical Discriminant function analysis indicated that external digital learning services informed digital divide in two school clusters – the less advanced schools were not able to proactively transact external digital learning services into their schools.


Since our grid data contain many services, the system view to services’ interaction appears to be complex. We have reduced services to the following domains:

Innovative ICT learning
Classical ICT learning in computer class and lessons, factual learning
Centrally provided technology, connectivity and resources
Transactionally obtained technology, connectivity and resources
Norms and ownership of ICT related aspects
Training and professional learning for ICT
Open access to resources
Resources provided by external business
Maintenance, Security and monitoring
Incentives and motivation
Peer-learning, networking, sharing resources
Satisfied access to ICT and teaching competences
Collective Involvement to change management if ICT in organization
Authoritive ICT development in organization

Linear modelling with stepwise method with united dataset from Ghana and Georgia indicated school learning ecosystem factors that determine certain ICT learning to be prevailing in schools:

  • the predictors of classical ICT teaching in school learning ecosystem are the availability of services from types: Incentives and motivation, Authoritive ICT development in organization, Open access to resources
  • the predictors of innovative ICT teaching in school are the availability of services from types: Peer-learning, networking, sharing resources, Transactionally obtained technology, connectivity and resources, and Open access to resources

ict innovations in education

September 20, 2013

From ECTEL2013 keynote presentation by  Panagyotis Kampylis

  • the more innovative practices are, the more difficult is to scale them up
  • innovation ecosystem: there must be dynamical adaptations and adjustments
  • innovations are started as top down  but must contain bottom-up innovation
  • leadership for strategic alignment
  • support teachers and teacher autonomy
  • support at multiple levels (class, places, levels)
  • use technology, but put pedagogy first
  • experiential learning
  • partnership with industry
  • have better definition and assessment for 21st century skills
  • ecological framework for innovative scaling
  • ecological diversity of innovation fosters scalability

Spatial narratives in new media ecosystems

November 13, 2012

Two years ago i held a speech on Spatial narratives in Media Mutations conference in Bolognia. Now they will publish a book in italian, and i have rewritten my conference speech with The Shadow of The Wind example, which will appear in the book in italian. Here is the english version of the paper:


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.

Narrative new media ecosystems

May 23, 2011

This week i will be at the Media Mutations conference in Bolognia. The topic focuses on ecosystems.

There are several interesting talks, i hope to add some comments to this post from the site:

Martedì 24 maggio

William Uricchio (MIT – Utrecht University)
When Metaphors Slip Their Bounds

Massimo Scaglioni e Luca Barra (Università Cattolica del Sacro Cuore, Milano)
Narrazioni arginate. La “riappropriazione” della tv convergente da parte del broadcaster

Giovanni Caruso (Università di Udine)
Gabriele Ferri (Università di Bologna)
Riccardo Fassone (Università di Torino)
Mauro Salvador (Università Cattolica del Sacro Cuore, Milano)
Check in everywhere. Luoghi, persone, narrazioni, giochi

Giovanni Boccia Artieri (Università di Urbino Carlo Bo)
Narrazione diffusa: self publishing e racconto connesso

Lucio Spaziante (Università di Bologna)
Quasi-mondi e realtà mediale: modelli e ipotesi

Andrea Castellani
L’onda anomala. Il LARP (Live Action Role-Playing) come forma narrativa di tipo “topical wave”

Antonella Mascio (Università di Bologna)
Tv serial, moda, pop-fandom. Nuovi modelli di “cataloghi narrativizzati”?

Giulio Lughi (Università di Torino)
Gadget emozionali: oggetti narrativi fra comunicazione e tecnologia

Mercoledì 25 maggio

Roberta Pearson (University of Nottingham)
‘Good Old Index’ or The Mystery of the Infinite Archive

Hector Perez Lopez (Università Politecnica, València)
Game of Thrones: l’ecosistema prima della première (17 aprile 2011)

Agnese Vellar (Università di Torino) e Luca Rossi (Università di Urbino)
The Expanded Glee Narrative and the Emergence of Disperse Audience on YouTube

Enrico Menduni (Università di Roma 3)
Dalla Weltgeschichte alle saghe narrative. Narrazioni del reale nell’era dell’eterno presente

Dario Compagno
Tre nozioni della narratologia classica in crisi: diegesi, metalessi, immanenza

Kai Pata (Università di Tallinn)
Narratives as Spatial Stories

Luca Rosati (Università per Stranieri di Perugia) e Andrea Resmini (University of Boras)
Beyond Flatland. Dal prodotto all’ecosistema: un modello per la progettazione e l’analisi di spazi informativi multidimensionali

Paolo Bottazzini
Google: paradigmi e cronologie digitali

Elisa Mandelli (Università di Venezia)
Il museo come ecosistema narrativo: nuovi media e valorizzazione del patrimonio culturale

Nuria Lloret Romero (Università Politecnica, València)
Cultural Heritage and Augmented Reality. The Augmented Museum


Towards an Ecological Meta-Design framework for Open Learning Ecosystems

April 7, 2011

We are together with Mart Laanpere currently working with the theoretical paper: “An Ecological Meta-Design framework for Open Learning Ecosystems”

In this paper we will introduce the ecological Meta-Design framework for open learning ecosystems. Meta-design is designing the design process for cultures of participation – creating technical and social conditions for broad participation in design activities (Fisher et al., 2004). Such cultures of participation represent the new types of learners in open learning ecosystems. They are self-directed, largely autonomous, and take design initiatives in respect of their learning environments (Fiedler & Pata, 2009; Pata, 2009; Väljataga & Laanpere, 2010). Learning in the cultures of participation may be characterized as the process in which learner and the system (community, culture) detects and corrects errors in order to fit and be responsive. In this definition, learning process is conceptualized as largely self-organized, adaptive and dynamic. We assume that such learning follows the ecological principles, which have been effectively used to explain processes and systems in technology enhanced learning (Pór & Molloy, 2000; Crabtree & Rodden, 2007; Vyas & Dix, 2007; Boley & Chang, 2007; Vuorikari & Koper, 2009; Pata, 2009). Open learning ecosystem is an adaptive complex and dynamic learning system in which self-directed learners design their learning activities and follow open education principles by sharing freely over the internet knowledge, ideas, infrastructure and teaching methodology using Web 2.0 software. Without wishing to suppress down such a bottom-up self-emergence of eLearning designs, providing teachers in learning institutions with design solutions that enable them to regain some co-control in the learner-initiated activities and systems is needed.

In this paper we aim to describe how ecology principles form the baseline for Meta-Design of learning in open learning ecosystems. Such Meta-design principles are needed to provide teachers in open learning environments with new models for organizing learning courses that consider the design activities of the cultures of participation.

In this paper we propose that the ecological Meta-Design framework applies for open learning ecosystems that are adaptive and dynamically changing. Both focuses – the learning ecosystem evolution by end-user design, and nourishing the end-user design process by creating the scaffolds for designing (see Ehn, 2008; Fisher et al., 2004), are equally important aspects of ecological Meta-Design. In learning ecosystems autonomous learners continuously develop and dynamically change design solutions to support their learning. They incorporate into their personal learning environments different Web 2.0 tools, networking partners and artifacts, and monitor the state of the whole learning ecosystem to adapt their design solutions and learning objectives to the system and to other learners.
Teacher’s role in the ecological Meta-Design framework for open learning ecosystems is designing scaffolds and incentives for design activities of learners. For example teacher should:
a) monitor the evolution of the open learning ecosystem,
b) provide learners with the options that enhance and speed up the self-directed network-formation process (e.g. tags, mashups),
c) analyze the emerging affordances within the learning community, and provide analytical guidance for them aiding to make design decisions and selecting learning activities (e.g. social navigation, semantic navigation), and
d) seed learning activities into the open learning ecosystem that are based on self-organization (e.g. swarming).

We will provide an insight to the learning design models in which ecological principles have been used. Such learning designs provide us with different views for modeling the ecological Meta-Design process, and highlight important components of our framework.

The appropriate trends in learning design models, which should be considered are:

a) The open, community-driven, emergent and iterative activity sequences in the learning design process models, which are based on learner contribution (Hagen & Robertson, 2009);

b) The systemic model approach to learning designs, which considers interrelations between learners and teachers with the whole learning ecosystem, and enables to generalize and predict learning patterns (Rohse & Anderson, 2009) and system affordances (Pata, 2009);

c) The balance models of learning design focuses and aspects, that create conditions for independent, autonomous and self-directed learning (see Brockett and Hiemstra, 1991) according to the interpretivist and connective learning principles, and;

d) The eco-cognitive learning design models, which explain differentiated and contextually conditioned perception of learning affordances, that results in learning system evolvement by learner contribution and adaptation (Pata, 2009).

Some related slides: