A model for cultural pattern appropriation in learning

June 12, 2015

Recently we have worked with my colleague Tobias Ley, on the figure to describe how distributed cognition and pattern-formation associate with individual, collaborative and collective learning – A model for cultural pattern appropriation in learning.

Earlier we have described a model of pattern-appropriation and relations between epistemic and collective distributed cognition with Emanuele Bardone ( Pata & Bardone 2014).


Figure. Collective and epistemic distributed cognition ( in Pata & Bardone, 2014)

Then we took a step forward and validated this model together with Tobias Ley and Paul Seitlinger using the tagging data (Ley, Seitlinger, Pata, in press).

We found that:

– individual stabilization co-occurs with processes of enculturation

– artifact-mediated activity lead to formation and stabilization of individual patterns

collective stabilization is a result of individual pattern formation and artifact-mediated social feedback.


Figure. Coupling in pattern formation between Collective and Epistemic Distributed Cognition. ( in Ley, Seitlinger, Pata, in press)

In the third step we started to look how these phenomena happen in workplace learning situations. In the Learning Layers project several interviews with workers in construction and healthcare context, and the related sectorial networks have been conducted. We wanted to use these data to describe the workplace learning patterns. Analytically we first identified existing patterns ( as some practices), and then tried formalising these pattern names. This lead us understanding that central patterns relate solving workplace problems, which brings along knowledge maturing and requires scaffolding learning at individual, collaborative and collective level.

We assume that scaffolding learning and the knowledge maturing are two processes of how the individual, collective and collaborative learning systems influence each other through pattern formation and -appropriation.

As patterns we consider individually, collaboratively or collectively created repeated solutions to the problems that may appear in different contexts. Taking the distributed cognition stance we may see knowledge as a set of pattern activations.

Our model for distributed cognition, scaffolded learning and knowledge maturing (version 1):

Copy of Learning Across Levels of Analysis

Figure A model for cultural pattern appropriation in learning (ver. 1 developed by Ley & Pata, 2015)

Scaffolding (Vygotsky 1978; Wood et al. 1976) in this model is a process where appropriate guidance structures (scaffolds) are created to enhance individual, collaborative or collectives learning in a fading out manner as the individual, group or collective becomes able in solving certain problems. The neo-Vygotskian perspective in social constructivism assumes that the culture (the collective learning) gives for the individual and for the collaborative learning the cognitive tools needed for development. Vygotsky (1994) saw the environment as the source of person‘s development and not its setting. Vygotsky’s (1978) socio-cultural theory emphasizes social interaction and the relationships between individuals and assumes that cognitive development, including higher-order learning, is rooted in social interactions and mediated by abstract symbols. These are not created in isolation but rather are products of the socio-cultural evolution of an actively involved individual. Scaffolding in distributed cognition framework is supporting epistemic and collaborative distributed cognition, the coordinated functioning of the learner(s)’ cognitive, metacognitive and affective domains embraced by collaboratively and collectively emerging patterns (see Ley, Seitlinger, Pata, in press).


The learning of individuals can be scaffolded (Wood, Bruner, and Ross, 1976) in the zone of proximal development (Vygotski, 1978), that initially was defined as the unidirectional difference between what a learner can do without help and what he or she can do with help of knowledgeable other. In later studies the ZPD concept has been extended to several phenomena. ZPD is considered bidirectional in collaborative situations (Forman, 1989; Goos et al., 2002) and is the the learning potential in small groups where learners have incomplete but relatively equal expertise and where each partner who possesses some knowledge and skills requires the others’ contribution in order to make progress. Through the usage and development of personal and collective patterns in individual learning situations Ley, et al. (in press) and in collaborative situations (Rasmussen, 2001) the ZDP may appear between individual epistemic distributed cognition and the collaborative or collective distributed cognition. According to Valsiner (1987), the culture sets constraints through zone of promoted actions (ZPA) (such as collaborative or collective patterns), and the context of action and actual environment may constrain it even further through zone of free movement (ZFM). Scaffolding in a self-organized systems (socio-technical systems) is no more restricted to human expert and learner (Puntambekar & Hubscher, 2005), but the accumulated knowledge and human behaviours in socio-technical system can be used as scaffolds (Lytras & Pouloudi, 2006; Tammets et al., (2014) and individuals, groups and  organizations must adapt themselves to the current dynamic state of the system. In socio-technical systems scaffolds appear as self-organized services created in synergy of social behaviours and technical means ( e.g. meaning making by social tagging). Socio-technical scaffolds are by nature meta-designed patterns that evolve through feedback loops involving the users in providing support elements to their problems (see Fisher et al., 2007).

In our model scaffolding process supports the knowledge maturing and learning processes that happen in parallel. Scaffolding process involves the following elements:

1) agents that receive and agents that provide scaffolding (individuals, groups, networks, organizations/collectives, socio-technical systems)

2) actions how scaffolding is requested for and put in action (noticing dissonance/mistakes, request for scaffolding; awareness; negotiation/grounding; (re)contextualization; validation/recognition;  uptake/re-experience)

3) scaffolding knowledge and how this is created  (scaffolding knowledge also is developed through the maturing cycle)

4) the problem and the associated knowledge patterns (a pattern is a personally or collaboratively or collectively validated solution to the problem)

5) the stages and relations of scaffolding agents and the problem that have to be detected to make scaffolding actions  applying scaffolding knowledge) effective (such as comprehension of agent’s goals in respect to problem

The scaffolding process can be described as follows:

The agent(s) (person, group, collective) that solve the problem have awareness of expected state of knowledge (the awareness of collaborative or cultural patterns) but notice the dissonance between their actual state of problem solving and the expected state (that is ZPD in the ZPA and ZFM). Agent)s) request for scaffolding and the agent(s) that scaffold (person, group, collective, socio-technical system) must be aware of such scaffolding requests. Then follows the process to discover the agent’s state in respect to problem (and related patterns) and scaffolding knowledge (and related patterns). This process requires agents to (re)contextualize the problem. Noticing the dissonance/detecting the mistakes between the scaffolded agents‘ and scaffolding agents‘ choice of patterns for solving the problem leads them to negotiate/ground for common understanding. This bases on the discourse act model (see in Traum and Allen, 1994, Clark and Schaefer 1989; Pata, 2005).


This (re)contextualization and negotiation/grounding process is done in the fading out manner, that requires the agent that scaffolds to have the dynamic awareness of the changing state of scaffolded agent’s patterns to solve the problem. (Re)contextualization and grounding also may require the remediation of the problem. Validation/recognition is part of the negotiation/grounding acts, it uses the individual, collaborative and cultural patterns to to provide recognition to the current problem solving event and moderates/finalizes grounding acts until the final solution is achieved. In the end of scaffolding process the agent that requested for scaffolding is able to uptake the practice, has the ownership of certain collaborative or collective pattern and no more scaffolding for solving this type of the problem is needed.

Knowledge maturing can be understood as a process where knowledge patterns from the individual level are taken up in the collaborative or collective ways through embodied cognitive processes to create collaborative or collective patterns that in turn influence individual, collaborative and collective learning ( Ley et al, in press). Maturing can be explained by trialogical learning (Hakkarainen and Paavola, 2009; Paavola and Hakkarainen, 2014) that describes the systemic (with feedback loop) nature of the knowledge maturing. Trialogical learning paradigm (Hakkarainen and Paavola, 2009; Paavola and Hakkarainen, 2014) takes the distributed cognitive stance and contextualizes the usage of digital tools and -artifacts in the organizational knowledge creation processes. Activities in organizations always contain various artifacts (e.g. instruments, procedures, methods, laws, forms of work organization etc.). Main core of trialogical learning approach is organizing work around shared knowledge artifacts as mediators of human thought and behaviour (Nardi, 1996) – the emergent interactional resources (Stahl, 2012), which can mediate between individual learning, group cognition and organizational knowledge building, will structure the shared work and reflective practices, may be versioned and iteratively transformed during long term knowledge creation, leading to forming organizational knowledge and practices. By capitalizing on distributed cognition (Hutchins, 1995), the trialogical approach examines knowledge artifacts as materially embodied entities that are worked on in various “external memory fields” (Donald 1991) and “activity systems” (Engeström, 1999) rather than reduced to their conceptual content only (Paavola and Hakkarainen, 2009). In order to transform knowledge artifacts as instruments of their activity, participants have to go through a developmental process of “instrument genesis” (Ritella and Hakkarainen 2012). Passing the knowledge artifacts from one technology to another and one social formation level (individual, group, collective) to another requires its remediation as a central practice (Paavola & Hakkarainen, 2014), and allows improving new properties in that knowledge. Remediation in maturing process is done by changing the format of knowledge from implicit to explicit, from practiced behaviours to verbally/visually communicated and written documents. In this remediation process knowledge is taken from individual to socially shared and collectively approved formats, it is formalized and standardized using governance mechanisms. Governance structures are also responsible for knowledge circulation in a systemic manner, enabling the access to matured knowledge (vocabularies, norms, guidelines etc.).

According to Schmidt and Kunzmann (2014), the knowledge maturing process actions can be divided between different agent levels in our model.

Individual learning phase

The   initial   phases  of maturing  (I.   Emergence)  are   characterized   by   the  exploration
(Ia)  of  new  spaces,  either  as  activities  of  analyzing existing  material  or  by  creative  processes  (new  ideas).  In  both  cases, knowledge is deeply subjective, and the individual decides through appropriation (Ib)   where   or   not   to   pursue   further
development of the usually abundant items in phase Ia. From the distributed cognition approach to learning (Ley et al., in press), appropriation contains individuals to appropriate collaborative or collective patterns, that can be supported by scaffolding processes.

Collaborative learning phase

In the next phase (II. distribution in communities), where knowledge gets discussed and negotiated between different individuals of a social group. This includes the development of a

shared vocabulary and associated understanding, and usually many individual contributions get amalgamated. To reach beyond the social group, transformation (III) is required where the focus is on creating artefacts by restructuring and agreeing on. Transformation means that knowledge is restructured and decontextualized to ease the transfer to collectives other than the originating community. Providing shared vocabulary is governance element as well.

Collective learning phase

For further outreach, the introduction phase (IV) provides an initial step in which either knowledge is prepared in a way that it is easier to understand for others as part of workshops or trainings (instructional strand) or put to practice in a pilot (such as process knowledge). Both is experimental and is a learning phase where experiences are incorporated that prepare for a wider roll-out in the institutionalization phase (Va) where the knowledge gets a stable place, either as part of formal training plans, or as company-wide implementations (processes, products or similar). The goal is here to gain efficiency. Both when knowledge is formalized to be understandable for all, and institutionalized are the elements of governance.

Finally, moving beyond the limited scope of companies, phase Vb (External standardization)

moves towards standardisation or certification where comparability and compliance play a primary role.

Knowledge maturing incorporates several components:

1) agents that create and use knowledge (individuals, groups, networks, organizations/collectives, socio-technical systems)

2) actions how knowledge maturing is initiated and managed (

exploration, appropriation, noticing dissonance/mistakes (that is important at individual, group and collective level and triggers maturing), request for maturing;  awareness (awareness is needed in the distributed cognitive framework to be aware of collaborative collective patterns and awareness allows appropriation) ; negotiation/grounding and (re)contextualization; validation/recognition;  formalization, standardization, uptake/re-experience)

3) knowledge, how it is represented and its maturity states (incorporating knowledge patterns – personally or collaboratively or collectively validated solutions to the problem)

4) the problem and the associated knowledge patterns

It is particularly interesting that according to our approach scaffolding adds to trialogical learning design (Paavola & Hakkarainen, 2009; 2014) this mediation component that allows cultural pattern appropriation in learning.

In the next phase we try to validate our model with the workplace learning data. Then we can be more certain, also can we use same action names for scaffolding and knowledge maturing processes in workplace learning.


Social recognition in pattern networks among professionals

April 22, 2015

This paper paper Pata, K., Santos, P., Burchert, J.”Social recognition provision patterns in professional Q&A forums in Healthcare and Construction” has been accepted to Computers in Human Behavior.


We recently studied with colleagues Patricia Santos and Joanna Burchert several professional forums from the point of view of learning, maturing and recognition.

We used SNA to reveal the patterns and their interrelations.


Here is an example pattern network figure based on one healthcare practitioners forum discussions.

Based on those pattern networks we composed a model of social recognition provision in forums.

This model relates practicing and learning at work, peer-support practices in forums, the validation practices with knowledge maturing and formalizing loop.


Increasing validation practices can improve the credibility accumulation of persons as experts in the network and the credibility of shared resources, and allow crowds to initiate sharing and localising practices, maturing of guidelines and rules.

Yet, in current professional forums, such recognition practices happen quite seldom, and forums are not very good places for discovering which practices are credible based on social recommendation. Bringing some practices to the maturing and formalising loop would require particular orchestrated effort from the networks.

Noticeable is that in face-to-face practices among professional quite similar pattern trends could be modelled. For example based on interviews with construction workers the following dependency model between actions was found.



The Set as an ecological concept

April 20, 2015

Today Terry Anderson http://terrya.edublogs.org
had a lecture of open education in Tallinn University.

He mentioned a genial and catchy The Set concept they have started using in MOOCs that i became quite facinated of.

“The Learning in Sets” chapter in the book Teaching Crowds ( 2014) by Terry Anderson and Jon Dron.

A set is defined by intentional engagement around a topic.

Much set-based learning occurs “just in time,” concerned with finding out something of value to the learner now, rather than a continuing path.

The set will represent a range of perspectives and views of the subject, which together will offer diverse opportunities to connect existing knowledge to new discoveries.

The Set may be a set of students who have similar preferences to courses they take in MOOCs, also i think in general on what they choose as resources. So in this way it resembles user profiles in recommender systems.

Terry mentioned they now try putting contacts between those in the Set.

The main reason i like the Set concept is it is pre-cultural, assuming that self-organised people will not necessarily discover each other, and supporting this intentionally is a meta-design principle that helps learning ecosystems to develop kind of culturally similar “species” who can then dedicate intentional efforts in communicating to learn from each other.

It is a question, when actually one becomes aware of certain Sets and identifying his belonging to this or that Set. And what will by the person’s awareness of other Sets.

Exposing ( discovery) of different sets and opposing different Sets for some problem-solving or other purposes can probably be done only with meta-design using analytics’ based feedback.

Going back home with tram from Terry’s lecture i was thinking of the Set as kind of distributed cognitive phenomenon – manifesting its existence through its externally created behavioural niche. Terry and Jon write:

Another way that sets can aid serendipitous discovery is when we spot trends or patterns in behaviour.

The set has proven to be surprisingly effective for connecting those in need with those who wish to give.

We have recently discussed of those niches containing the patterns (as culturally defined solutions to problems) developed by certain “cultures” or in other words by Sets, as well as how individual pattern formation in epistemic distributed cognition happens embedded to the cultural pattern formation.

set distributed cognition

I was also thinking if the formation of Sets was promoted in open educational courses, what way it would change the currently too much individualistic MOOCs. And what kind of learning analytics and learner focused feedback could be given for Set formation as another meta-design element that promotes learning ecosystem management. Terry and Jon write:

Self-referentially, the Set itself can provide resources and clues about the reliability of information found within it, particularly if it incorporates collective tools that emphasize reputation, provide ratings, or show other visualizations that give hints about the value of a contribution or individual.


An artifact ecosystem – new socio-technical regime for eTextbooks

August 17, 2014

I have just presented my ideas in Kai Pata. Mart Laanpere, Maka Eradze eTextbook as an artifact ecosystem in Future eTextbooks – FeT workshop in ICWL 2014.

Full paper: http://link.springer.com/chapter/10.1007/978-3-319-13296-9_25

Kristo Käo and Margus Niitsoo. MatchMySound. Introducing Feedback to Online Music Education.

o Kai Pata, Mart Laanpere and Maka Eradze. E-textbooks: towards the new socio-technical regime. (c-map of etextbooks as artifact ecosystems)

o Mario Mäeots, Leo Siiman and Margus Pedaste. Designing Interactive Scratch Content for Future E-books

o António Pedro Costa, Luis Paulo Reis and Maria João Loureiro. Hybrid User Centered Development Methodology: An Application to Educational Software Development.

o Andrej Flogie, Vladimir Milekšič, Andreja Čuk and Sonja Jelen. Slovenian “E-school bag”.

o Maka Eradze, Terje Väljataga and Mart Laanpere. Observing the use of e-textbooks in the classroom: towards “Offline” Learning Analytics.

o Terje Väljataga and Sebastian Fiedler. Re-conceptualizing E-textbooks: in Search for Descriptive Framework.

o Arman Arakelyan, Ilya Shmorgun and Sonia Sousa. Incorporating Values into the Design Process: The Case of E- Textbook Development for Estonia

Our paper with Maka and Mart discusses the niche technologies that have and possibly will contribute to the future e-textbooks as a new socio-technical regime. We propose the conceptual map of textbook functionalities aiming at opening the conceptual discussion for brainstorming and finding scenarios how the niche technologies that explored novel textbook applications in learning might be best combined into the new “artifact ecosystems” regime. Jointly with workshop participants we aim to come up with metaphors and concepts depicting learning in this regime.

Full papers are published in http://www.springer.com/computer/book/978-3-319-13295-2


Promoting distributed cognition at MOOC ecosystems

July 4, 2014

In June i attended the HCII 2014 conference in Crete where i presented our paper with Emanuele Bardone.



exploring systemic cognition empirically

April 17, 2014

Activity description:

We have in theory camp the need to combine some theoretical aspects for developing a theory framework for social semantic server, action design approach.
We can use wordpress with KB widget for discovering collectively created resources.
Every participant uses in the first stage KB to search and tag suitable resources. Each resource gets the assignment tag (e.g. social semantic server task has tag social semantic server, or action research has task action research) and as many additional tags as one would need to describe the meaning dimensions of added resources. Each resource added to KB is also shared with others.
In Social semantic server we may have to types of knowledge resources emerging:
– personal resources (personal cognitive niches) around the assignment tag (for example person A has collected resources, which have additional tags systemic cognition, knowledge maturing; whereas person B has tagged resources additionally with communities of practice, scaffolding, adaptive learning etc.).
– shared resources around the assignment tag ( this compiles all resources that have tag social semantic server, as well as the associated tags and presents this tag-set to each user) (collective niche).
In the second phase of the activity, wordpress is used as the meaning-making tool. Each participant has in wordpress the widget with the KB tags. This widget enables to see personal tag-cloud with a certain assignment tag, and shared tag-cloud with the certain assignment tag. Each person uses shared tags to search from resources and uses liked tags and resources for elaborating the question, how social semantic server activities can be explained with different theoretical perspectives.
The meaning-making activity ends with tagging the reflections with the assignment tag and other additionally selected tags. Basically, these matured resources become part of the shared pool of knowledge and may be used in the next iterations of the same activity.
Research questions about systemic cognition: How is the shared knowledge explored and made use of by individuals at meaning-making?
Does personal knowledge (personal cognitive space) determine, which tags are perceived as relevant in the shared tagcloud? We can compare personal tags that are related with assignment tag with the activated tags in the shared tag-cloud around the assignment tag to discover if there is dependency.
Do the most frequently utilized tags in the shared tagcloud (accumulated patterns in the collective niche) get more attention for selecting resources? We can compare most frequent tags with the activated tags in the shared tag-cloud around the assignment tag to discover if there is dependency.
How does the personal cognitive space mature as a result of using collective cognitive space? We can compare initial (phase1) and final (phase 2) tagsets of elaborated resource tags of individuals – is there convergence happening between individuals due to pattern-usage? Can the convergence happen after some cycles of the activity?
Explanation: systemic cognition is the distributed cognition in which personal cognitive space is embedded to the collective cognitive space. However we do not know how collective knowledge space becomes activated due to personal cognitive spaces (basically how culture influences what we decide).
Additionally, in KB some resources can be rated by giving stars. How would such rating increase the visibility and usage of certain tags (basically can the collectively validated resources be chosen more frequently) – basically can we enhance pattern-visibility in the culture, and whether such patterns be
We may try to play such an activity among ourselves in HTK, or among the colleagues in Layers.
The same idea in variations in topics may be done with application partners. For example we can choose a problem-related tag as the central assignment tag. We may also try to play the same activity with students in association with some idea, central concept, but this is better doable in autumn term as part of some master or doctoral course.

systemic cognition and support in socio-technical systems

March 19, 2014

Explaining informal learning@work at managed clusters organized as TEL based socio-technical systems requires binding different level explanations: distributed cognitive level, personal – organizational level, cross-organizational network – cluster level.


Systemic (or distributed) cognition level

Benefits: Focusing on the systemic nature of distributed cognition (on the interplay between the epistemic distributed cognition from the agents’ side and the collective distributed cognition of organizational or professional community cultures) allows using the ecosystem principles for describing how learning services emerge and co-exist in this informal workplace learning ecosystem.

Distributed cognition makes use of vector-spaces for describing cognitive niches of individuals, cultural niches and meaning niches of resources.

Person- Organization level

Benefits: to open up the transformative knowledge conversion between individual and organizational knowledge (Nonaka & Takeuchi, 1995); particularly utilizing agents’ informal learning events for the benefit of organization and motivating self-directed learning at work with social and (cross-)organizational factors.

Problems: Implementing new learning cultures in organizations, moving from unintentional towards intentional informal learning practices in organizations

Cluster and cross-organizational network level

Benefits: increased responsiveness for the cluster and for its member organizations is achieved through temporal cross- and inter-organizational informal learning activities at work, and orchestrated bottom-up and top-to-down systemic management of shared knowledge and provision of services based on the knowledge base (see IntelLEO project results for responsiveness).

Problems: competitive edge between members, sharing restrictions for knowledge, the lack of mutual trust or over-conficence in one’s organization’s knowledge

Workplace learning ecosystems

Basically, the systemic cognition approach views socio-technical systems at workplace learning as learning ecosystems.

There is a variety of learning services at present (created by experts and in general by any learner), which are used by other informal learners and that accumulate and interact at organization’s and cluster’s knowledge-bases.

Agents: novices and experts:

Scaffolding in networks requires considering the differences of agents’ problem contexts, knowledge and expertise.

Self-directed agents create and make use of (request for, validate, share, modify etc.) workplace learning service exemplars when they solve problems or provide help.

Each learning service exemplar provided or utilized must be fit to the prototypical learning services niche of his kind. These niches are determined by many exemplars that agents activate. For example, request for help must contain sufficient information about the specific problem and help needed to attract those help-providers that have suitable expertise for tackling this problem, further, the help provided to meet this request must be useful, it should solve this problem as closely as possible.

Knowledge transfer is primarily inter-personal.

Organization: At socio-technical system level certain prototypical learning services are dynamically provided, depending on which learning services the agents activate:

  • increased awareness for, accepting and forwarding help-requests;
  • providing help adaptively in turn-taking actions that ground the problem;
  • fading out the help when competence increases;
  • indicating towards developing helpful resources (artifacts, objects, tools, persons in the network);
  • validating resources;
  • increasing persons’ expertise and trust level in respect of providing help for learning at work.

Each prototypical learning service is directed towards solving some workplace problem or conceptualizing some idea. These prototypes have contextual meaning niches that emerge and change dynamically as a result of many agents’ activation of the exemplars of that kind. These meaning-niches are like communicative signals offloaded to the socio-technical system. They serve as attraction basins indicating to agents, where organizational learning could be most effective.


  • creates incentives and manages motivation for promoting learning cultures at work;
  • explores and incorporates to organizational practices the usage of new learning@work activities;
  • removes the restrictions for cross-organizational knowledge transfer;
  • promotes open innovation cultures – open access to early prototypes,
  • design solutions or process-innovations with open source licenses;
  • promotes temporal alliances between members from different organizations to identify how to cope with challenges;
  • explore the opportunities or develop innovation.

Cluster management: maintains cluster’s organizational networks and knowledge base (ontologies, competences, norms and guidelines, access to human and virtual-real learning resources) and provides services based on this knowledge:

  • distributes information about challenges, practices and opportunities to learn;
  • identifies and nourishes new ideas that arise in the cluster organizations – such as organizing temporal cross-organizational knowledge-building activities for innovation;
  • provides, evolves and matures professional norms and guidelines;
  • initiates service-based value networks between member organizations;
  • detects proximities between cluster members;
  • promotes the learning culture at work that increases social motivation  – the more users are involved, the more likely it is that system becomes effective and is self-organizing;
  • controls organizational learning with incentives and motivation-management (policies for accreditation possibilities, and validation of workplace learning experiences).

Formal and informal cross-organizational networks are important to transfer knowledge.

The learning services the cluster can initiate depend on the abundance of certain learning service exemplars and of the learning service prototypes and niches at present in the socio-technical system.

Ecosystem principles applicable in learning ecosystems

The first principle in ecology is that the flow of energy and the exchange of matter through open ecosystem is regulated by the interactions of species (in our case types of learning services) and the abiotic component (by the web of energy and matter). Reyna 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).

The second important ecological principle is existence of the feedback loop to and from the environment 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 elaborates the notion of niche construction as an ecological factor that enables organisms to contribute for and benefit from environmental information. If organisms evolve in response to selection pressures modified by themselves and their ancestors, there is feedback in the system. In our approach to digital learning ecosystems, the “service-species” are activated by users with different roles (learner, facilitator) and their learning intentions. The niches for each service-species in the digital ecosystem may be collected from user-behavior, for example by learning analytics (an emerging approach to tracing digital footprints of learners and groups, visualizing the learning-related patterns).

Applications in social semantic systems: 

Niches are vector spaces – see paper From vector spaces to meanings

If we make use of the Connectionism approach to concept-processing (see the paper of Seitlinger et al, 2013) and extend this approach to epistemic and collective distributed cognition that happens in using mobile learning tools together with social semantic server, we may have an approach for socio-semantic recommendations that provide help based on the meaning niches that fit best to the requests (see the examples below).

In biology the figures for niche breadth figures are used, that may be useful in recommendation, also the idea of fitness landscape and attraction basins may be considers in recommendations.

The third important principle that we extend from ecology to technology-enhanced learning domain 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 or commensalism 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.

Application cases of informal learning at work

Below, there are three informal learning and supporting behaviours that may potentially appear in socio-technical systems.


To introduce new knowledge to the newcomers the experts make use of their earlier experiences, they also utilize and evolve resources for providing help, as well as the archetypical scaffolding models in their profession, on the other hand, the help-provision increases the trust level of experts in respect of solving certain problems.ACCUMULATING EXPERTISE


Recommending – relating systemic cognition and connectionist approaches

Some issues of recommending when using the systemic (distributed) cognition approach:recommending

Seitlinger. et al., (2013). Recommending Tags with a Model of Human Categorization

Seitlinger et al.(2013) use in their recommendation model Connectionist model of cognitive processing:

Kruschke 1992 alcove model

First layer can have distributed activation. The model is initialized with equal attention strengths to all dimensions, but as the training proceeds, the model learns to allocate more attention to relevant dimensions and less to irrelevant dimensions.

Internal layer functions as agent’s cognitive niche that incorporates cultural niche for weighting. Internal layer gives weights to the nodes, each hidden node corresponds to a position in the multidimensional space. A state of activation (a) at a given time (t): The state of a set of units is usually represented by a vector of real numbers a(t). These may be binary or continuous numbers, bounded or unbounded. A frequent assumption is that the activation level of simple processing units will vary continuously between the values 0 and 1.

In biology, 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 case prototypes). Niches have been conceptualized as the collections of environmental gradients with certain ecological amplitude, where the ecological optimum marks the gradient peaks where the organisms (in our case exemplars) are most abundant.

The welfare of species can be determined by meaning-creation and action-taking possibilities in the environment.

In the gradient concept structural ecosystem properties are comprehended as concentration gradients in space and time (Müller, 1998). Any niche gradient is a peak of the fitness landscape of one environmental characteristic (Wright, 1931), which can be visualized in two-dimensional space as a graph with certain skew and width, determining the ecological amplitude. The shape of the fitness graph for certain characteristic can be plotted through the abundance of certain specimen (exemplar in our case) benefitting of this characteristic. All niche gradients are situated and establish a multi-dimensional hyper-room, which axes are different environmental parameters.

This connectionist theory problem was also explained by T. Ley in Innsbruck meeting.

Also see article From vector spaces to meanings