Archive for the ‘elearning 2.0’ Category


An ecological learning design approach

November 15, 2013

I have summarized my last years ideas into the Ecological Learning Design approach.

This is the paper that i am going to present and test out on my colleagues in Tallinn University where our research is focused on various (digital) learning ecosystems.

The shorter version of it is in the slides


thesis: Learning and knowledge building practices for teachers’ professional development in an extended professional community

July 7, 2013

Kairit Tammets, my first doctoral student will defend her thesis at 21st of August.

Now her dissertation is available from here

Tammets, Kairit (2013) Learning and knowledge building practices for teachers’ professional development in an extended professional community.

The purpose of her PhD research project is to investigate the process of the learning and knowledge building (LKB) in the extended professional community that is supported with the socio-technical system.

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


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.


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:


Grant report: Distributed learning environments, their interoperability, and models of application (2008-2013)

April 7, 2011

From 2008-2013 our team in Tallinn University, Center for Educational Technology was fulfilling the grant “Distributed learning environments, their interoperability, and models of application”.
Now it is the time to make some conclusions:

We developed the framework and tool prototypes for supporting self-directed learning in augmented learning environment. An augmented learning environment is defined as such merging traditional learning environment and a virtual learning environment together with various technological tools and social software.


I. A model of self-directed learning where learners are involved in development of their personal learning environment was created. Mainly bachelor and master level students were involved in performing the empirical studies. A conceptual framework for designing learning courses which focus on the development of competences of self-directed learners is developed.

Several experiments in authentic course settings were conducted:
a) the course for self-directed learning with social software in TLU,
b) the international course of eLearning with iCamp project partners, and
c) the course Narrative ecology in TLU.
The students’ visually- and verbally-presented self-reflected feedback to the learning environments and activity patterns was collected from the augmented environments.
The following analytical results were achieved:
a) Learners’ perspectives to self-directed learning were identified (Pata & Merisalo, 2009; 2010)
b) Instructional design aspects of self-directed learning were outlined (Fiedler & Pata, 2009; Pata & Merisalo, 2009; 2010, Väljataga, Pata, Tammets, 2010)
c) Learners progress in self-direction using particular indicators was described (Pata & Merisalo, 2009; 2010)
d)The new swarming behaviors of creating personal and collaborative narratives in augmented environment as the self-generative phenomenon, and the changes in the storytelling standards were identified and explained using the ontospace approach (Pata, 2009; 2010;2011).

These empirical results provided an input to determining the characteristics of learning design framework for self-directed learning in augmented learning environments (Fiedler & Pata, 2009; Pata, 2009a,b; Pata, 2010a,b; Pata & Merisalo, 2009; Pata & Merisalo, 2010; Pata, 2011; Normak, Pata, Kaipainen, submitted; Pata & Laanpere, submitted).

The main characteristics of an ecological framework to learning design for self-directed learning in augmented learning environments adopt the ecosystems principles for describing pedagogical processes in learning space. The framework assumes the following: The nature of education is changing and the prioritization of learning experiences from informal and non-formal education, besides formal education, is expanding the range of learning options. Beyond the boundaries of formal higher education individuals have to structure and carry out their activities without a support of educational authorities. Therefore, in parallel to teaching domain related knowledge, formal higher education should create opportunities for students to practice and advance their dispositions for self-directing intentional learning projects. Giving students increased control over crucial instructional functions may be achieved by promoting self-directed individuals, who are capable of updating their knowledge and skills outside of formal educational systems. Therefore, instructional design should rather be seen as an intervention design of challenging situations with placement of constraints. An emerging personal learning environment (PLE) approach to augmenting learning environments emphasizes learner control over an environment and networking. An elaborated understanding of PLE integrates important instructional functions of learner control as an expression of self-direction and gives an opportunity to talk about self-directing intentional projects, in which an individual is provided with much higher control over his project and environment. Setting up one’s PLE in relation to a particular learning project has two sides: it requires a certain degree of learner control and on the other hand it also helps to practice dispositions.
The educational changes towards self-directed learning in augmented spaces have two major implications:
1) The possibility for individually differentiated use of learning tools, methods and freedom in selecting personally relevant learning goals requires more self-direction from the learners in order to satisfy individual learning needs, and
2) For achieving maximal results individually, there is a need to discover how learners with similar goals belonging to the appropriate learning community would conduct their learning, and orientate one’s activities accordingly.
We suggest that learning has certain analogies to how an individual specimen of any species adapts itself to the niches of its species in the natural ecosystems. Inspired by this analogism, we apply the concepts and methods of ecology for studying and designing learning processes. We assume that learning design process forms an iterative continuous cycle:
a) In one phase one learning community defines dynamically their learning niche (or the niche of similar previous community /course/ could be used);
b) In another phase the conditions for the re-appearance of this learning niche will be supported by instructional designers by preserving and making the activity- and meaning traces created during the real activities of initial community available for the next communities. According to this model, learners and facilitators participate ecologically in the niche construction, changing the learning space and causing the evolution of learning. At the same time, they can use social navigation in the community’s learning niche to guide their individual learning actions. The basic steps of an ecological learning design framework for supporting self-directed learning in augmented learning environments are:
1. Define the learning and teaching niches for your students by collecting their affordance perceptions of their learning spaces dynamically in the course of action.
2. To support the conscious self-managed development of learner-determined spaces, provide students with the tools of visualizing and monitoring their activity-patterns and learning landscapes, and enhancing public self-reflection and collaborative grounding of learning affordances.
3. To maintain coherence of the current niche, introduce cycles of re-evaluation of learning affordances of the learning space within your course.
4. Try to influence the niche re-emergence by embedding activity traces and ecological knowledge relevant to evoke affordances for certain niches or select activity systems where these traces are naturally present.
5. Use same social learning environments repeatedly to gain from feedback left as activity traces and embodied knowledge of earlier learners.

Paper The Ecological Meta-Design framework for Web 2.0 Learning Ecosystems by Pata & Laanpere is in progress. In this paper we summarize the learning patterns of self-directed learners in different augmented settings and propose the design framework.

Related papers:

Kieslinger, Barbara, Pata, Kai (2008). Am I Alone? The Competitive Nature of Self-reflective Activities in Groups and Individually. ED-MEDIA 2008 – World Conference on Educational Multimedia, Hypermedia & Telecommunications. Vienna, Austria, June 30-July 4, 2008. (6337 – 6342).AACE

Tammets, Kairit; Väljataga, Terje; Pata, Kai (2008). Self-directing at social spaces: conceptual framework for course design . Ed-Media, Viin, 30. juuni-4. juuli, 2008. AACE, 2008, 2030 – 2038.

Fiedler, S.; Pata, K. (2009). Distributed learning environments and social software: in search for a framework of design. Stylianos Hatzipanagos and Steven Warburton (Toim.). Handbook of Research on Social Software and Developing Community Ontologies. (145 – 158).Idea Group Reference

Pata, K. (2009). Modeling spaces for self-directed learning at university courses. Journal of Educational Technology & Society, 12, 23 – 43.

Väljataga, Terje (2009). Selecting tools and services:an expression of self-direction in higher education. In: The Proceedings of the 8th European Conference on e-Learning: 8th European Conference on e-Learning, Bari, Italy, 29-30. Oct. 2009. (Toim.) Dan Remenyi. UK: Academic Publishers, 2009, 665 – 671.

Pata, K.; Merisalo, S. (2009). Self-direction indicators for evaluating the design-based eLearning course with social software . Kinshuk; D.G.; Sampson; J.M. Specor; P.Isaias; D.Ifenthaller (Toim.). IADIS International Conference on Cognition and Exploratory Learning in Digital Age CELDA 2009 (196 – 203). Rome: IADIS Press

Väljataga, Terje (2009). If a student takes control: facilitator’s tasks and responsibilities. In: Advances in Web Based Learning – ICWL 2009: 8th International Conference, Aachen, Germany, August 2009. (Toim.) Marc Spaniol, Quing Li, Ralf Klamma, Rynson W.H. Lau. Germany: Springer Heidelberg, 2009, (LNCS 5686), 390 – 399.

Väljataga, Terje; Pata, Kai, Tammets, K. (2010). Considering learners’ perspectives to personal learning environments in course design. J.W. Lee; C. McLoughlin (Toim.). Web 2.0 Based E-Learning: Applying Social Informatics for Tertiary Teaching (85 – 108).IGI Global

Pata, K.; Merisalo, S. (2010). SELF-DIRECTION INDICATORS FOR EVALUATING THE DESIGN-BASED ELEARNING COURSE WITH SOCIAL SOFTWARE. Dirk Ifenthaler, Dr. Kinshuk, Pedro Isaias, Demetrios G. Sampson, J. Michael Spector (Toim.). Multiple Perspectives on Problem Solving and Learning in the Digital Age (343 – 358).Springer

K.Pata & M.Laanpere, An Ecological Meta-Design framework for open learning ecosystems, ECER 2011, “Urban Education”, konverents (accepted).Berlin, Germany from 13th to 16th September.(accepted)


II. An ecological approach to learning dynamics was developed that bases on the idea of dynamically evolving learning space that is described by certain ontological coordinates and terms borrowed from physical ecosystems.

In the ecological framework of learning design model we have described in detail using the spatio-dynamic ontospatial methods, how individual learners would determine their learning paths in the community learning space.

The following analytical results were achieved:
a) Learners’ perception to their individual and collaborative learning niches with social software using affordances was described (Pata, 2009a,b; Väljataga, Pata, Tammets, 2010) and formalized using dynamic ontospatial methods (Normak, Pata & Kaipainen, submitted).
b) Learners’ individual and collaborative perspectives within the shared ontospace in hybrid ecosystem were characterized (Pata, 2010).

We assume that new approaches to emergent learner-directed learning design can be strengthened with a theoretical framework that considers learning as a dynamic process.
We propose an approach that models a learning process using a set of spatial concepts: learning space, niche, perspective, state of a learner, step, path, direction of a step and step gradient. A learning process is presented as a path within a niche (or between niches) in a learning space, which consists of a certain number of steps leading the learner from the initial state to a target state in the dynamically changing learning space. When deciding on steps, the learner can take guidance from learning paths that are effective from a viewpoint of the learning community.

Kaipainen, M.; Normak, P.; Niglas, K.; Kippar, J.; Laanpere, M. (2008). Soft ontologies, spatial representations and multi-perspective explorability. Expert Systems, 25(5), 474 – 483.

Pata, K. (2009). Revising the framework of knowledge ecologies: how activity patterns define learning spaces? . Niki Lambropoulos & Margarida Romero (Toim.). Educational Social Software for Context-Aware Learning: Collaborative Methods & Human Interaction. (241 – 266).Idea Group Reference

Pata, K.; Fuksas, A.P. (2009). Ecology of Embodied Narratives in the Age of Locative Media and Social Networks: a Design Experiment. Cognitive Philology, 2, 1 – 21.

Pata, K. (2010). An ontospatial representation of writing narratives in hybrid ecosystem. In: Proceedings: Workshop on Database and Expert Systems Applications: 21st International Workshop of DEXA, 3rd International Workshop on Social and Personal Computing for Web-Supported Learning Communities. August, 30th – September, 3rd 2010, Bilbao, Spain.. (Toim.) A.M.Tjoa and R.R.Wagner. Los Alamitos, California: IEEE Computer Society Press, 2010, 87 – 91.

Pata, K. (2011). Participatory design experiment: Storytelling Swarm in hybrid narrative ecosystem. B. K. Daniel (Toim.). A Handbook of Research on Methods and Techniques for Studying Virtual Communities: Paradigms and PhenomenaHershey. New York (482 – 508). Hershey. New York: Information Science Reference

Normak, P., Pata, K. & Kaipainen, M. (submitted). An Ecological Approach to Learning Dynamics.


III. A new conception of a distributed learning environment that supports self-directed learning was elaborated. Based both on empirical and theoretical research, software prototypes „EduFeedr“ (for managing web based courses) (; , „LePress“ (for assessing learning outcomes in blog based personal learning environments) and „LeContract“ (for composing and management of learning contracts) are developed. The developed conception of distributed learning environment was taken as the basis in designing a new generation distributed learning management system

Tomberg, Vladimir; Laanpere, Mart (2009). RDFa versus Microformats: Exploring the Potential for Semantic Interoperability of Mash-up Personal Learning Environments. In: Mashup Personal Learning Environments: MUPPLE 09, Nizza, 29.September 2009. (Toim.) Fridolin Wild, Marco Kalz, Matthias Palmér, Daniel Müller . Aachen:, 2009, (CEUR Workshop Proceedings; 506).

Põldoja, Hans (2010). EduFeedr: following and supporting learners in open blog-based courses. In: Open ED 2010 Proceedings: Open Ed 2010 – The Seventh Annual Open Education Conference, Barcelona, 2.-4. november 2010. Barcelona: UOC, OU, BYU, 2010, 399 – 407.

Leinonen, Teemu; Purma, Jukka; Põldoja, Hans; Toikkanen, Tarmo (2010). Information Architecture and Design Solutions Scaffolding Authoring of Open Educational Resources. IEEE Transactions on Learning Technologies, 3(2), 116 – 128.

Põldoja, Hans; Väljataga, Terje (2010). Externalization of a PLE: Conceptual Design of LeContract. In: The PLE Conference: The PLE Conference, Barcelona, 8.-9. juuli 2010. Barcelona:, 2010.

Tomberg, Vladimir; Laanpere, Mart; Lamas, David (2010). Learning Flow Management and Semantic Data Exchange between Blog-based Personal Learning Environments. G. Leitner, M. Hitz, and A. Holzinger (Toim.). HCI in Work & Learning, Life & Leisure – USAB 2010 (340 – 352). Berlin: Springer Verlag

Tomberg, Vladimir; Laanpere, Mart (2011). Implementing distributed architecture of online assessment tools based on IMS QTI ver.2. Lazarinis, Fotis; Green, Steve; Pearson, Elaine (Toim.). Handbook of Research on E-Learning Standards and Interoperability: Frameworks and Issues (41 – 58).Idea Group Reference