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

April 7, 2011

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

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


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

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

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

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

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

Related papers:

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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


III. A new conception of a distributed learning environment that supports self-directed learning was elaborated. Based both on empirical and theoretical research, software prototypes „EduFeedr“ (for managing web based courses) (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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