Meta‐design principles for open learning ecosystems Kai Pata Tallinn University, Ins;tute of Informa;cs Center for Educa;onal Technology MUPPLE Lecture Series, 2011
An overview • What is an eco‐cogni;ve view to learning? – The examples of open learning ecosystems in course design – The learning niches and how to use them in learning design – Why to use the meta‐design approach? – The meta‐design approach to open learning ecosystems – Some developments and limita;ons for meta‐ design from the soFware side
Open learning ecosystems • Open learning ecosystem is an open, adap;ve complex and dynamic learning system in which self‐directed learners design their learning ac;vi;es and follow open educa;on principles by sharing freely over the internet knowledge, ideas, infrastructure and teaching methodology using Web 2.0 soFware.
Par;cle and system level views to open learning ecosystems Learners in open learning “Ecosystem” not as a metaphor ecosystems: • Self‐direc;ng • Self‐regula;ve • Networking, PLEs, PLNs • “Connec;vist Networks” in open learning ecosystems • Communi;es of open • “Community” of digital cultures educa;on • Co‐designing and sharing • Open, Dynamic and Evolving • Accumula;ng • Monitoring • An evolu;onary feedback loop • Adap;ng • Naviga;ng
Towards the learner‐centered approach • Two pedagogical paradigms have been highlighted in open learning ecosystems. • Firstly, the interpre;vist learning principles suggest that students should be: – guided towards becoming independent, autonomous and self‐directed learners) – who learn from being ac;vely engaged in the situa;ons that are meaningful to them, from their interac;ons with peers and teachers in which they are given the voice so they that they can become co‐ constructers of the learning environment – and by reﬂec;ng on their prior knowledge and experiences to construct new meanings
An example course ICamp interna;onal eLearning course (Pata & Merisalo, 2010)
Towards the learner‐centered approach • Secondly, for cul;va;ng the “ecosystem” view in digital systems (see Pór & Molloy, 2000; Crabtree & Rodden, 2007; Boley & Chang, 2007), George Siemens (2006) formulated the Connec;vism framework as the new learning theory for open learning ecosystems. • Connec;vism assumes that: – Learning is primarily a network‐forming process, and the dynamically appearing and changing networks form basis for the learning ecosystems
An example course Ecology of narra;ves course (Pata & Fuksas, 2009 hap://padis2.uniroma1.it:81/ojs/index.php/cogphil/ar;cle/view/4338/4200
A gap in design principles for open learning ecosystems • Without wishing to suppress down such a boaom‐up self‐emergence of eLearning designs, we should provide teachers in learning ins;tu;ons with design solu;ons that enable them to regain some co‐control in the learner‐ini@ated ac@vi@es and systems is needed (Fiedler and Pata, 2009).
A gap in design principles for open learning ecosystems • The teachers’ need to control the learning design and learning process in distributed systems • The necessity to allow freedom for learners to be self‐directed and using their own personal learning environments in higher educa;on courses
Oﬄoading cogni;ve func;ons to the digital ecosystem • Humans constantly delegate cogni@ve func@ons to the environment (Bardone, 2011)
An eco‐cogni;ve view to learning • Human cogni@on is chance‐seeking system that is developed within an evolu;onary framework based on the no;on of cogni@ve niche construc@on. • We build and manipulate cogni@ve niches so as to unearth addi@onal resources for behavior control. • Human cogni;ve behavior consists in ac@ng upon anchors – the aﬀordances* (*see Gibson, 1977) ‐ which we have secured a cogni@ve func@on to via cogni;ve niche construc;on. (Bardone, 2011)
Distributed cogni;on and aﬀordances Learning as a cogni;ve niche Previous ac;on environment as experiences in this digital ecosystem environment Teachers’ goals Learners’ goals and and perceived instruc;ons for ac;on ac;on poten;ali;es A cogni;ve niche Schema adopted from Zhang, J., & Patel, V. L. (2006)
Learning aﬀordances and PLE • PLEs are dynamically evolving Ac@vity systems* (*see Engeström, 1987) in which the personal objec@ves and human and material resources are integrated in the course of ac;on. • PLE is also distributed ecologically, integra@ng our minds with the environment (see Zhang & Patel, 2006; Bardone, 2011). Diﬀerent learning goals assume the percep;on of diﬀerent aﬀordances in PLE
Aﬀordances as a networked system? • Aﬀordances may constrain each other • Synergy may be arrived from using several aﬀordances simultaneously • Some aﬀordances may need the presence or the co‐ac;va;on of other aﬀordances to be used eﬀec;vely • Using one aﬀordance may actualize another aﬀordance in the network
Deﬁning community niches by aﬀordances • People determine the personal learning aﬀordances within their PLEs. • Any individual conceptualizes aﬀordances personally, but the range of similar learning aﬀordance conceptualiza;ons visualizes community’s preferences – a community learning niche
The learning niches • Adapta;on ‐ the adjustment of an organism to its environment in the process by which it enhances ﬁtness to its niche • The forma;on of learning niches in open learning ecosystems appears through accumula;ng learning aﬀordances from PLEs (Pata, 2009). • The community’s aﬀordances may be interpreted and used by each learner to best adapt to the community niche for certain goal‐based ac;on
Earlier models that use aﬀordances in learning design Learner’s role is passive, design is created by the teacher. Learning environment not dynamically evolving. Kirschner et al. (2004)
Why meta‐design approach? • The ecological Meta‐Design framework applies for open learning ecosystems that are adap;ve and dynamically changing. • Meta‐design is designing the design process for cultures of par;cipa;on – crea;ng technical and social condi;ons for broad par;cipa;on in design ac;vi;es (Fisher et al., 2004). • The meta‐design approach is directed to the forma;on and evolu;on of open learning ecosystems through the end‐user design.
Meta‐design approach • The meta‐design approach is known as a methodology for collabora;ve 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 tradi;onal no;on of system development to include users in an ongoing process as co‐designers, not only at design ;me but throughout the en@re existence of the system (Fisher et al., 2004).
Meta‐design approaches • Autonomous and self‐organized designers in meta‐design framework can increase the diversity of design solu;ons in the system, allowing diversity and variability to emerge within the ecosystem. • The open, community‐driven, emergent and itera;ve ac;vity sequences in the learning design process models are based on learner contribu;on (Hagen & Robertson, 2009).
Ecological principles in meta‐design • Learning in the cultures of par;cipa;on 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 (Fisher et al., 2004). • In this deﬁni;on, learning process is conceptualized as largely self‐organized, adap@ve and dynamic. • It may be assumed that such learning follows the ecological principles
Some principles of meta‐design • Both focuses – the learning ecosystem evolu@on by end‐user design, and nourishing the end‐user design process by crea;ng the scaﬀolds for designing (Fisher et al., 2004), are equally important aspects of ecological Meta‐ Design.
Suitable The process view to meta‐design in open learning ecosystems (Pata, 2010)
Learners’ role • In learning ecosystems autonomous learners con;nuously develop and dynamically change design solu;ons to support their learning. • They incorporate into their personal learning environments diﬀerent Web 2.0 tools, networking partners and ar;facts, and monitor the state of the whole learning ecosystem to adapt their design solu;ons and learning objec;ves to the system and to other learners.
Teachers’ role • Providing the teacher‐created scaﬀolds and incen;ves for the learners design ac;vi;es that would foster the accumula;on of learning niches: – a) monitor the evolu;on of the open learning ecosystem, – b) provide learners with the op;ons that enhance and speed up the self‐directed network‐forma;on process (e.g. tags, mashups), – c) analyze the emerging aﬀordances within the learning community, and provide analy;cal guidance for them aiding to make design decisions and selec;ng learning ac;vi;es (e.g. social naviga;on, seman;c naviga;on), and – d) seed learning ac;vi;es into the open learning ecosystem that are based on self‐organiza;on (e.g. swarming).
The limita;ons for applying meta‐ design in an open learning ecosystem • There is the need for dynamic accumula@on and monitoring systems for learning niche forma@on to be used by each learner for beneﬁ;ng from par;cular open learning ecosystem and allowing them to par;cipate in the course design – accumulated aﬀordances and their dissipa;on in ;me (community‘s learning niche) – real‐;me awareness of aﬀordances for other learners (their cogni;ve niches)
Connec;vity with PLE components (Dippler, Tallinn University development) Distributed course assembling tools (Dippler) Learning contract tools (LeContract, Learning creator (Siadty et a.l, 2011),) User monitoring, accumula;on and visualiza;on Suitable Monitoring tools (EduFeedr (Põldoja & Laanpere, 2009)) The tools to support meta‐design in open learning ecosystems (Pata, 2011)
Kai Pata senior researcher, Tallinn University, Ins;tute of Informa;cs, Center for Educa;onal Technology email@example.com, blog hap://;hane.wordpress.com
References • Bardone, E. (2011). Seeking Chances. From Biased Ra;onality to Distributed Cogni;on. Springer, Heidelberg. • Boley, H., & Chang, E. (2007). Digital Ecosystems: Principles and Seman;cs, published at the 2007 Inaugural IEEE Interna;onal Conference on Digital Ecosystems and Technologies. Cairns, Australia. February 2007. NRC 48813. • Crabtree, A., & Rodden, T. (2007). Hybrid ecologies: understanding interac;on in emerging digital‐physical environments. Personal and Ubiquitous Compu;ng, Online First: DOI 10.1007/s00779‐007‐0142‐7. • Fiedler, S,; Pata, K. (2009). Distributed learning environments and social soFware: in search for a framework of design. In Stylianos Hatzipanagos and Steven Warburton (Eds.). Handbook of Research on Social SoFware and Developing Community Ontologies. (145 ‐ 158). Idea Group Reference. • Engeström, Y. (1987). Learning by Expanding: An Ac;vity‐Theore;cal Approach to Developmental Research ( hap://communica;on.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, Ac;ng and Knowing. Hillsdale, NJ: Erlbaum. • Hagen, P. and Robertson, T. (2009 Dissolving boundaries: social technologies and par@cipa@on 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 collabora;ve learning environments. Educa;onal Technology Research and Development 52(3), 47–66. Pata, K. (2009). Modeling spaces for self‐directed learning at university courses. Educa;onal Technology & Society, 12 (3), 23–43. • Pata, K., Fuksas, A. P. (2009). Ecology of Embodied Narra;ves in the Age of Loca;ve Media and Social Networks: a Design Experiment. Cogni;ve 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.). Mul;ple Perspec;ves 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 Educa;onally Enhanced Mash‐up Tool for Agora Courses. In: Mashup Personal Learning Environments: MuPPLE 09, Nizza, 29.September 2009. (Eds.) Fridolin Wild, Marco Kalz, Maahias Palmér, Daniel Müller. Aachen (online):, 2009, (CEUR Workshop Proceedings; 506). • Siadaty, M., Gasevic, D., Pata, K., Milikic, N., Holocher‐Ertl, T. (2011). A Sema;c 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. hap://www.knowingknowledge.com/2006/10/knowing_knowledge_pdf_ﬁles.php • Zhang, J. & Patel, V.L. (2006). Distributed cogni;on, representa;on, and aﬀordance. Distributed Cogni;on: Special issue of Pragma;cs & Cogni;on 14, 333‐341.
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