Archive for the ‘hybrid ecology’ Category

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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

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Spatial narratives in new media ecosystems

November 13, 2012

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

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thinking of the nature of ecology of mind

May 2, 2012

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

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

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

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

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

  • ecological knowledge
Heft (2001) wrote that: “we engage a meaningful environment of affordances and refashion some aspects of them… These latter constructed embodiments of what is known – which include tools, artifacts, representations, social patterns of actions, and institutions – can be called ecological knowledge. Ecological knowledge through its various structural, material culture, human setting manifestations becomes an integral social and cultural part of ‘the environment’, with these social and cultural affordances constituting effective, largely material, forms of knowledge with their own functional significance, cultural transmission, and adaptation implications.”
Heft, H. (2001). Ecological psychology in context. : James Gibson, Roger Baker, and the legacy of William James’s radical empiricism. Lawrence Erlbaum Associates, Publishers.
It also associates with the cultural interface  concept (Manovich, 2008).
  • cultural interface
Lev Manovich in Software takes control (2008), uses cultural interface concept to describe human-computer culture
The interface is habitually the crucial boundary, or zone of articulation and translation whenever a computer would communicate with technological devices or the human user
“Cultural interfaces”, not just the diverse software interfaces of new media but also the formal traits and user practices with the printed word and cinema, can migrate into, and become part of, the interfaces of new media.
“While operations [like selection] are embedded in software, they are not tied to it. They are employed no only within the computer but also in the social world outside it. They are not only ways of working within the computer but also in the social world outside it. They are… general ways of working, ways of thinking, ways of existing the computer age.”
  • distributed cognitive system
I had to analyze our IntelLEO project results (cross-organizational leaning and knowledge-building supported by technological services), and as we have considered that it may be seen as a distributed cognitive system.

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

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

June 9, 2011

Here is an advertisement to my lecture at MUP/PLE Lecture series podcast, at Open University of London. The podcast will be soon available.
Mash-UP Personal Learning Environments (MUP/PLE) group on TELeurope: http://www.teleurope.eu/pg/groups/681/mupple/.

Lecture text muppletext(should be accompanied by slides):

Abstract:
From last five years, many master courses in Tallinn University, Institute of Informatics have been held as open learning courses using social software. Based on this experience with different learning design experiments, I have generalized the meta-design principles for open learning ecosystems.
My main message is how to overcome the need for the teacher control in self-regulative learning ecosystems by using meta-design principles.
I will start from the analysis of the characteristics of open learning ecosystems, and bring some examples of course designs that follow these characteristics.
As the baseline for the design of the courses in open learning ecosystems I have used the eco-cognitive view of learning. This is based on the ecological psychology foundations defined by Gibson (1977) that focus on the emergent relationships of people with the environment using the affordance concept.
For explaining which affordances each learner perceives and uses when he participates at the open learning courses with his personal learning environment, the activity theory framework developed by Yrjö Engeström (1987) appears useful.
Some central ideas of my approach are borrowed from behavioural ecology, which studies the fitness of individuals to the niches of their species. An eco-cognitive approach explains cognition through distributed representations that are partially offloaded to the ecosystem. I will conceptualize the learning niches and explain how to use them in learning design.
Next, I will introduce some meta-design approaches that involve the end-users to the development of evolving learning designs.
I will explain how the meta-design approach may be used for the course design in open learning ecosystems.
Finally, I will point to some innovative tools that we have used in our open learning master courses, and highlight some existing software limitations for the accumulation and adaptive use of learning niches in meta-design approach.

1. Learning in open learning ecosystem

An open learning ecosystem is a digital (but also a hybrid) learning environment where learners and teachers use personalized social software configurations to organize their learning.
Open learning courses are open to new learners. The learning contents, as well as, the teaching ideas, the design methodologies and infrastructures are jointly developed and openly shared among this community.
This brings variability of tools and approaches to the courses, making the learning environments complex and dynamically changing while learners try to adapt themselves to the course ecosystem.

We use the ecosystem concept, because it allows us to apply the principles of ecology in digital learning environments. If we want to apply the ecology principles in digital systems, we need to find the match between the ecology concepts and the components of digital ecosystems.

Ecology as a discipline deals with different levels of structural elements of ecosystems, both biotic and abiotic.

For example, behavioural ecology focuses on the individuals of the species and their fitness to the niche of their species. Etology studies the interrelations of individuals.
In digital ecosystems we have self-directed individual learners who create personal learning networks with other individuals, using social software, people and artifacts. In order to coexist, they need to monitor each other, navigate across their learning environments, and try to adapt themselves to the other individuals’ useful activity preferences within the shared learning niche.

Population ecology studies the variability, the abundance and the distribution of individuals within one species, and how the species adapt to their niches, create and modify these niches.
In digital ecosystems we have people with similar ideas, software preferences and behaviours for learning, who may be identified as one community or “species”. They contribute to their learning niche by co-designing and sharing learning contents, and by developing new learning behaviours.

The community ecology focuses on the coexisting communities of species, their composition, interactions, organization and succession, as well as, on the food networks among species.
In digital systems we can find similar self-regulative connectivist networks, and communities that co-exist in the same distributed software ecosystems, but using different, partially overlapping niches of it. This makes borrowing and transforming the ideas across community borders possible and creates the learning power in digital ecosystems.

The most important assumption about open learning ecosystems is that the individuals’ self-directed learning behavour, personal learning environment creation, and open publishing causes the ecosystems to be open, dynamic and evolving. At the ecosystem level the accumulation of contents, useful learning behaviours, and ideas causes the changes in the ecosystem and in the community identities, that serves as an evolutionary feedback loop that impacts on individual learners’ perceptions of their learning ecosystem.

Two pedagogical paradigms have been highlighted in open learning ecosystems.
Firstly, the Interpretivist learning principles suggest that students should be guided towards becoming independent, autonomous and self-directed learners. Their learning must rise from their own interests and situations meaningful for them.
It is important that they are not isolated but interact with other learners, acting also as teachers to the others. The learning contents, and software usage behaviours are not created in advance but are emerging and co-created as network-like structures. Every learner can contribute with its prior knowledge and experiences to the creation of open ecosystem knowledge, everyone has the voice and ability to influence the ecosystem.
This guarantees the self-regulative and evolving nature of open learning ecosystems.

One example of such course design was done in the European 6th framework project iCamp for the course eLearning. The learners and teachers from different European universities created the mixed learning teams, to learn about open learning designs and create the course prototypes and associated learning resources about their design solutions. The course backbone was run in the Moodle environment, from where the suggested learning resources and weekly activities could be found. However, all learners and teachers entered to the course with different sets of personal tools, which were to be connected into the open learning system for conducting individual and joint activities. The changes and evolvements of the course ecosystem appeared in different teams. The biggest challenge was to design and coordinate the course as an evolving open learning ecosystem.

Another pedagogical paradigm in open learning ecosystems is Connectivism formulated by George Siemens.
Connectivism assumes that:
Learning is primarily a network-forming process, and the dynamically appearing and changing networks form basis for the learning ecosystems

This approach cultivates the ecosystem view of digital systems. I define open and hybrid digital ecosystems based on Boley and Chang (2007). It is an open, self-organizing environment binding geographical and web based locations, individuals, social software based information services, network interaction and knowledge sharing tools along with resources that help maintain synergy among people, where each subject is proactive and responsive regarding its own benefit/profit.

One example master course, “Ecology of narratives” that used the Connectivism ideas was run in Tallinn University (Pata & Fuksas, 2009). The learning design approach was built on the idea of initiating the emergent narrative collaboration using only the self-regulated storytelling activities. The main element of this design was to provide learners with some design rules, such as determining shared tags, and restricting the behavioural rules from traditional pre-decided group collaboration to emergent co-construction in networks. The co-construction emerged due to highly connected networks created among course participants who were using friend-feeds, place-feeds and mashed tag-feeds in various interconnected social software environments.

2. Challenges for learning design

These two examples highlight the main problem in the learning design for open learning ecosystems.
We need the learning design approaches that enable teachers to regain some co-control in the learner-initiated activities and in the appearing open ecosystems for learning.

In one hand we do want learners to be self-directed in creating learning goals, developing learning activities with personal tools, and choosing and constructing learning contents. We wish to promote the bottom-up emergence of the learning ecosystems.
On the other hand, we need to coordinate our courses to some extent at the universities, if we want to use the distributed learning environments.

The theoretical background for designing open learning ecosystems comes from ecological psychology. It is assumed that for interacting with the environment, we need some cognitive anchors. Humans constantly delegate cognitive functions to the environment. We may leave these anchors by ourselves, anchors may be left by other people who interacted with these surroundings before, these may be for example culturally defined. By doing so we constrain the action potentialities of the environment and help to focus on certain action- or emotion possibilities. But it must be admitted, that such cognitive functions are not stabile design elements in the environment – we cannot assume that everyone would perceive these affordances, or that they would afford the same actions and emotions.

3. An eco-cognitive learning framework

Bardone (2011) emphasizes this ambivalence in developing these cognitive functions. He writes that human cognition is chance-seeking system that is developed within an evolutionary framework based on the notion of cognitive niche construction. We build and manipulate cognitive niches to create additional resources for behavior control. These cognitive niches are determined by affordances.
Cognitive niches are distributed between internal mental spaces and external spaces in the environment. Behavioural/emotional constraints and afforded action/emotion potentialities may appear due to previous action/emotion experiences of the learner in this or in similar environment. Learners’ actual goals may highlight and actualize some affordances. Some affordances may be embedded/highlighted by teacher through instructions or may appear due to the presence of other learners’ activities. Each learner has a different cognitive niche in certain activity, and it may change in the course of action.
This causes high variability of affordances that may be actualized in open learning ecosystem for same learning goals.

Another standing-point to identify, which affordances might be actualized in a personal learning environment, is using the activity system approach (Engeström, 1987).

PLE is distributed ecologically, integrating our minds with the environment. We may assume that at each moment a different configuration of the activity system is active. To reach our learning goals we need to actualize different mediators, such as cognitive concepts, details from artifacts, software, or rely on some community activities. Rules and distribution of labor, common to the certain community, as well as, their personal learning environments, ideas, and the joint ecosystem structural elements may afford different mediators to be available for achieving certain actions or emotions.

Affordances in our cognitive niche form a networked system. They may constrain or actualize each other. Synergy may be arrived from using several affordances simultaneously.
Some affordances may need the presence or the co-activation of other affordances to be used effectively.

An interesting aspect from the learning design perspective is that some of the affordances are offloaded to the ecosystem.
While any individual conceptualizes affordances personally, in a community such perceived and offloaded affordances may accumulate, forming the community’s learning niche.
This niche conceptualization is closer to the niche concept in biology. Hutchinson (1957) defined a niche as a region (n-dimensional hypervolume) in a multi-dimensional space of environmental factors that affect the welfare of a species. Niches have been conceptualized as the environmental gradients with certain ecological amplitude, where the ecological optimum marks the gradient peaks where the organisms are most abundant.
So, niche is not the environment itself, the habitat, but what it affords. For example the range of temperature, the length of daylight, the abundance of certain food form the dimensions for a niche for certain species.
In digital systems these dimensions may associate with the properties of certain activity systems: for example complexity of assembling, accumulating, pulling content; degree of reputation, privacy, security, surveillance, interaction, co-construction in the community etc.
Currently, there are no good tools to monitor these affordances, nevertheless the community members perceive some of the learning niche properties

In one of my open learning ecosystem courses (Pata, 2009) we asked students to associate self-defined affordances with social software. We grouped these affordances under some activity types. As a result we could find what types of affordances were more commonly perceived by everyone, and which appeared to be rare. To make a visualization of the community’s learning niche we plotted the niche as the map of affordance “mountains”. However, this visualization is also a bit misleading – the real niche should be plotted into an abstract multidimensional space. This visualization does not consider the affordances that appear due to the presence of other affordances. For example, some software functionalities (tagging) may make available other affordances only if a community uses them actively (browsing the community members’ resources).
We also found in this study, that at different years the community niche of social software affordances appeared similar. On the other hand, for individual and collaborative assignments the niche dimensions were significantly different.

So I propose that the accumulated community niches for different learning goals may indicate the effective affordances for certain communities. The community’s affordances may be interpreted and used by each learner to best adapt to the community niche for certain goal-based action. Adaptation is the adjustment of an organism to its environment in the process by which it enhances fitness to its niche.
Such interplay in which each individual contributes to the formation of his cognitive niche, but also to the accumulation of the community’s niche, and simultaneously adjusts his affordance perception to his community niche is the central idea in dynamic evolving learning ecosystems.
It is one of the key points in meta-design framework as well.

4. Ecological learning designs as meta-designs

There have been some attempts to use the affordance concept in the learning design principles. This model from Kirchner and associates (2004) determines learners’ perceived affordances from their behaviour in the learning system, develops supportive and constraining affordances for interacting with the system and monitors the effectiveness of such affordance-based cognitive tools. However, learner’s role in this design approach is passive, the design is created by the teacher. Also, the dynamic evolvement of the learning environment is not expected. So, this model has limitations from the open learning ecosystem design perspective.

Another design approach, that considers adaptive and dynamic nature of the ecosystems is a meta-design framework proposed by Gerhard Fisher (2004) and associates. Meta-design is designing the design process for cultures of participation – creating technical and social conditions for broad participation in design activities. The meta-design approach is directed to the formation and evolution of open learning ecosystems through the end-user design.

The meta-design approach is known as a methodology for collaborative co-design of social, technical and economic infrastructures in interdisciplinary teams in order to achieve synergy similarly to the symbiosis phenomena in natural environments. The meta-design, known from End User Design in computer science, extends the traditional notion of system development to include users in an ongoing process as co-designers, not only at design time but throughout the entire existence of the system.

Autonomous and self-organized designers in meta-design framework can increase the diversity of design solutions in the system, allowing diversity and variability to emerge within the ecosystem.
Hagen & Robertson visualize in their paper some meta-design models as open, community-driven, emergent and iterative activity sequences that are based on user contribution.

For example:
Figure 1. The design solution is iterated through the participation in use. For example the e-learning course may be run at different years, the affordances may be collected post-activity, as in Kirchner’s model, and the revisions of the course ecosystem could be made. If letting the accumulated contents and affordances inside the system, the next round of the course may be able to navigate in the ecosystem better than the first participants.

Figure 2. The design emerges into different directions through participation. Such designs in open learning communities may be validated by individuals, and the best, most actively used solutions will become temporally stabile as community niches. This type of design is common in swarming activities.

Figure 3. The initial design may be outsourced to the users, and appears as an assembled collage. This approach is more common in open content creation, for example in wikis.

Figure 4.The design may also be opened to the community participation throughout design, that may gradually shift the perceived niche of affordances. Such design is most common in the stabile open learning communities – some affordances will be discovered, others will be forgotten in time.

Similarly to behavioural ecology principles, in meta-design we can see the interplay between self-directed individual designers (in our case learners), and the niche (the affordances of the design solution) that appears as the result of their activities.
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 and designing process is conceptualized as largely self-organized, adaptive and dynamic.
It may be assumed that such learning and meta-design follows the ecological principles.

Both focuses – the learning ecosystem evolution by end-user design, and nourishing the end-user design process by creating the scaffolds for designing, are equally important aspects of ecological Meta-Design. Such scaffolds may be the visualizations of the emergent community’s learning niche.

To make some generalizations from our master courses in open learning ecosystems, the following aspects might be important in the meta-design framework
Learners should be facilitated to be self-directed. For this they are required to keep personal conversational learning contracts throughout the learning process. For example they could map their goals, and how they will achieve these goals, what affordances appeared useful in action.
Learners need to dynamically integrate their personal learning environments with the other learner’s environments, in order to perform some joint tasks or allow better awareness of each other’s activities. The affordances perceived during the course may change depending of their goals.
In order to better adapt to the digital ecosystem, the learners would need meta-level guidance.
For example, the rules and conditions (shared tags etc.) that facilitate niche accumulation may be determined by the teacher. The nature of activities may be selected such that supports self-regulation-based collaboration (for example swarming activities).
The learners and the teacher should be able to monitor the state of the niche, and can adjust their learning behaviours to the niche.

So, what would the learners do?
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.

What is the teacher’s role?
The teacher creates rules, scaffolds and incentives for the learners’ design activities that would foster the accumulation of learning niches
These include:
Possibilities for monitoring the affordances of the community
Providing learners with the options that enhance and speed up the self-directed network-formation process (e.g. tags, mashups)
Analyzing the emerging affordances within the learning community, and providing analytical guidance for them aiding to make design decisions and selecting learning activities (e.g. social navigation, semantic navigation)
Seeding learning activities into the open learning ecosystem that are based on self-organization (e.g. swarming)

Some of these designs are well supported with suitable software for open learning ecosystems. However, there is the need for dynamic accumulation and monitoring systems for learning niche formation to be used by each learner for benefiting from particular open learning ecosystem and allowing them to participate in the course design
Two options may be used:
The affordance informations should be accumulated dynamically, and this information, if well visualized, would help navigation of individual learners in the learning niches
The real-time awareness of the other learners‘ perceived affordances may appear in the systems where users are constatntly at present (such as facebook wall or twitter), however this is more time-consuming way to deduce the learning ecosystem affordances

References for slide texts

•Bardone, E. (2011). Seeking Chances. From Biased Rationality to Distributed Cognition. Springer, Heidelberg.
•Boley, H., & Chang, E. (2007). Digital Ecosystems: Principles and Semantics, published at the 2007 Inaugural IEEE International Conference on Digital Ecosystems and Technologies. Cairns, Australia. February 2007. NRC 48813.
•Crabtree, A., & Rodden, T. (2007). Hybrid ecologies: understanding interaction in emerging digital-physical environments. Personal and Ubiquitous Computing, Online First: DOI 10.1007/s00779-007-0142-7.
•Fiedler, S,; Pata, K. (2009). Distributed learning environments and social software: in search for a framework of design. In Stylianos Hatzipanagos and Steven Warburton (Eds.). Handbook of Research on Social Software and Developing Community Ontologies. (145 – 158). Idea Group Reference.
•Engeström, Y. (1987). Learning by Expanding: An Activity-Theoretical Approach to Developmental Research (http://communication.ucsd.edu/MCA/Paper/Engestrom/expanding/toc.htm).
•Fischer, G., Giaccardi, E. Ye,Y., Sutcliffe,A.G., Mehandjiev, N. (2004). META-DESIGN: A MANIFESTO FOR END-USER DEVELOPMENT. COMMUNICATIONS OF THE ACM September 2004/Vol. 47, No. 9 (33-37 .
•Gibson, J.J. (1977). The theory of affordances. In R. Shaw & J. Bransford (eds.), Perceiving, Acting and Knowing. Hillsdale, NJ: Erlbaum.
•Hagen, P. and Robertson, T. (2009Dissolving boundaries: social technologies and participation in design. Proceedings of OZCHI 2009, ISBN: 978-1-60558-854-4
•Kirschner, P., Strijbos, J. W., Kreijns, K., Beers, P. J. (2004). Designing electronic collaborative learning environments. Educational Technology Research and Development 52(3), 47–66.
•Pata, K. (2009). Modeling spaces for self-directed learning at university courses. Educational Technology & Society, 12 (3), 23–43.
•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.; 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 (Eds.). Multiple Perspectives on Problem Solving and Learning in the Digital Age (343 -358).Springer
•Pór, G., & Molloy, J. (2000). Nurturing Systemic Wisdom Through Knowledge Ecology. Systems Thinker, 1 (8), 1–5.
•Põldoja, H., Laanpere, M. (2009). Conceptual Design of EduFeedr – an Educationally Enhanced Mash-up Tool for Agora Courses. In: Mashup Personal Learning Environments: MuPPLE 09, Nizza, 29.September 2009. (Eds.) Fridolin Wild, Marco Kalz, Matthias Palmér, Daniel Müller. Aachen (online):, 2009, (CEUR Workshop Proceedings; 506).
•Siadaty, M., Gasevic, D., Pata, K., Milikic, N., Holocher-Ertl, T. (2011). A Sematic Web-enabled Tool for Self-Regulated Learning in the Workplace. iCALT 2011 proceedings (xxx-xxx). Athens, Georgia, USA: IEEE Computer Society Press [in press]
•Siemens, G. (2006) Knowing knowledge. URL. http://www.knowingknowledge.com/2006/10/knowing_knowledge_pdf_files.php
•Zhang, J. & Patel, V.L. (2006). Distributed cognition, representation, and affordance. Distributed Cognition: Special issue of Pragmatics & Cognition 14, 333-341.

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Narrative new media ecosystems

May 23, 2011

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

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

Martedì 24 maggio

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

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

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

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

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

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

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

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

Mercoledì 25 maggio

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

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

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

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

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

Kai Pata (Università di Tallinn)
Narratives as Spatial Stories

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

Paolo Bottazzini
Google: paradigmi e cronologie digitali

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

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

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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:

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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.

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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)

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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.

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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) (http://www.edufeedr.net/pg/edufeedr/faq; http://www.edufeedr.org/) , „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

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