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Digital learning ecosystem principles in socio technical systems

Ecological principles usable in sociotechnical systems

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Digital learning ecosystem principles in socio technical systems

  1. 1. Digital learning ecosystem principles in socio-technical systems Kai Pata Research group seminar, May, 2nd, 2017
  2. 2. Socio-technical system definition • Socio-technical system is an open system where self- organized users conduct collective operational tasks and the system undertakes the operational delivery of task objectives using the co-optimized social and technical sub-systems for task performance (Maquire, 2014) • Socio-technical affordances are the relational properties in particular situations of a specific user-technology system – these are action-taking possibilities and meaning-making opportunities in an actor-environment system with reference to actor competencies and technical capabilities of the socio-technical system (Vatrapu, 2009). • M. Maquire, (2014). Socio-technical systems and interaction design – 21st century relevance. Applied ergonomics, 45, 162-170. • R.K. Vatrapu (2009). Towards a theory of socio-technical interactions. Learning in the Synergy of Multiple Disciplines. LNCS 5794, 694-699 User agents Functionalities Flows FEEDBACK SUPPORT AGGREGATE INTERACT CONSTRUCT affordances
  3. 3. Examples of socio-technical systems that may be considered digital learning ecosystems • Portfolio-based learning communities (Tammets, Pata, Laanpere, 2013), we could see eDidaktikum as one • Professional practice sharing forums (Pata et al., 2016) • LePlanner authoring system (Pata et al., 2017; Beliajev, 2017), further we could see socio-technical systems composed around eSchoolbag, eDiary systems, authoring learning scenarios etc. • Open learning ecosystems (Laanpere, Pata, Normak, Põldoja, 2014; Pata & Bardone, 2014) such as we use for teachig • Digital service provision in schools (papers with Quaicoe, Jeladze) – example case in the end provides generalized approach in progress.
  4. 4. Teachers’ (T) and domain experts’ (O) socialization activities were categorized as S_T and S_O. Externalization activities were divided into ‘writing a blog entry’ E(Entry)T or ‘commenting on a blog’ E(Comment)T or E(Comment)O. Combination activities in the forum were categorized as C_T and C_O. In addition, the activity ‘view’ was categorized as View T and View O and this included viewing the weblogs, forums and materials. Bayesian network model of dependencies - Interdependence of teachers’ and domain experts learning and knowledge building interactions in Tammets, Pata, Laanpere, 2013
  5. 5. Interaction in professional forums indicating the communicative flows ( Based on Pata et al., 2016) Common for nurses Common in building sector
  6. 6. Digital Learning Ecosystem principles: components • Diversity of DLE components (broad definition of DLE community): variability within each type: • Humans (and digital agents) as DLE species - variability in characteristics and behavior due to their intentions, perceptions and cultural belonging ( narrow definition of a community) • Digital tools, artifacts as DLE species – variability in functionalities, content and form • Services as DLE species (lichen-like symbionts) • Kinds of components (relates with their positioning in transforming knowledge similar to autotrophes, heterotrophes, decomposers): may be agent interaction levels with DLE like consume, react, interact, expand, create etc.
  7. 7. Digital Learning Ecosystem (DLE) principles: structure • Network of kind of components, such as DLE services and agents • The permeability of a natural ecosystem for the circulation of energy and materials will depend on the nature of the 'architecture' of the components of the system, the connections in the trophic chains and the side-paths and hubs in the trophic web and characteristics of individual species. • Types of flows. Information flow, networking, learning flow, data flow • Agent level view: agent-agent interaction; agent-system interaction • System level view: structural (density, closeness of components), flows within the system, • Agent-system feedback loops: niches, signals, traces
  8. 8. DLE principles: communication- and transformation related flows • Agents’ attention and interaction to DLE creates different tool-, artifact-, service- activation and triggers flows (less services is more attention) • Agents use DLE services for passing knowledge (between same type of agents, across different types of agents – cross cultural agent communication) (hubs within DLE) • Agents use services for transforming knowledge in DLE (locational closeness of services, reactiveness across services) • Agents offloading some knowledge temporally to the DLE • Agents’ guided attention using DLE services to enact the offloaded signals, traces of knowledge • DLE piping the transformative flows between the service nodes • Transformation of information to new energy rich states and levels – maturing knowledge • Note. In ecology energy flow refers to the flow of energy through the trophic levels of food chain. At each trophic level about 90 % of energy is lost at metabolistic transformations. How much energy would DLE knowledge transformation loose at different knowledge maturity levels?
  9. 9. Affordance preferences of LePlanner as a socio-technical system (Beliajev, 2017)
  10. 10. DLE principles: transformation flow • DLE agents and services enable to permeate the transformative flow through the learning ecosystem • From “information” to “knowledge” (Frielick, 2004; Reyna, 2011). • Services may be differently activated by different types of agents, this may cause different temporal transformative flows to pass the DLE. • For different communities of agents different temporal niches may be activated within the DLE. • Niches within DLE provide areas of fitness for certain types of agents’ behavior, productivity (distance, coverage, overlap of niches) Frielick, S. (2004) Beyond constructivism: An ecological approach to e-learning. Proceedings ASCILITE 2004, 328-332. Reyna, J. (2011). Digital Teaching and Learning Ecosystem (DTLE): A Theoretical Approach for Online Learning Environments. Proceedings ASCILITE 2011, 1083-1088.
  11. 11. Affordance niches of LePlanner defined by users (Beliajev, 2017; Pata et al., 2017) Functionalities at different screens of LePlanner
  12. 12. DLE services - creativity, innovation • Innovation in the service system requires transforming the current communicative and transformative flows between the agents/ service nodes in DLE. • Creativity arises from an novel message translation/transformation between the agents/service nodes of the ecosystem, from creating new knowledge transformation paths • DLE agents and Service nodes possess volatility – reactivity of nodes • Service nodes can create a new service if there is high volatility, reactiveness • Reactiveness increases interconnectivity between agents/service nodes which in turn increases the transformation flow permeability in DLE • It requires extra energy being spent to reorganize, stabilize the DLEs
  13. 13. DLE principles: maturity states • Ecosystems undergo ecological succession (Golley, 1994). • Succession is a kind of DLE maturing process Succession of biological community • Odum (1969) proposed several energy-related trends to be expected in the growth and development of ecosystems from early to mature stages. • its physical structure increases • feedback increases (including recycling of energy and matter); • entropy production decreases at mature states • Information increases • Note. A measure to quantify maturity in ecosystems is proposed based on the analysis of flows of biomass (Perez-Espania, 1999). What flows could DLE use for maturity indicator? How to quantify measuring those flows?
  14. 14. Ecosystem effectiveness • Productivity - the ability of systems to accumulate energy in matter in time • Permeability - Lotka–Odum’s hypothesis states that an ecosystem develops towards maximizing power (Lotka, 1922; Odum and Pinkerton, 1955), interpreted as the highest possible throughflow of energy. • Entropy is kept lower within the system than beyond its borders, entropy (disorder)production is minimized • Smartness – directedness to successive changes
  15. 15. Ecosystem effectiveness: entropy • Entropy – a measure of “disorder” - is a measure of how organized or disorganized a system is, of the number of ways in which a system may be arranged (the higher the entropy, the higher the disorder). • Note. Biosystems may maintain local order (low entropy) within their system boundaries compared with the space around them. • Schrödinger (1944): Fairly high level of orderliness (= fairly low level of entropy) really consists of continually sucking orderliness from its environment”. • Energy needs to be spent to create order in DLEs, then entropy level decreases. Naturally, if systems increase and evolve (that is accompanied by innovation), there is more disorder, entropy level increases. • Note: To what extent, how frequently DLEs can afford innovation to restructure them, not that it results in energy overconsumption or new disordered system?
  16. 16. Ecosystem effectiveness: entropy • Ecosystems grow and develop in four progressive growth forms reflected in boundary, structure, network, and information relationships. • Boundary growth brings the input of low-entropy material into the system. • Exergy dissipation is dependent on the exergy capture or the ability of the ecosystem to divert a greater amount of low-entropy energy across its border. • Structural growth as a result of the increase in the amount, number, and size of components in the ecosystem • System with greater exergy is moved further from its reference state. • Exergy dissipation refers to the energy given off by breaking down the high quality, low-entropy energy (orderliness) for both growth and maintenance of the system. • Network growth is growth in connectivity of the system through additional energy–matter transactions and internal organization of the system • System connectivity and cycling increase through additional network transactions retains the energy–matter within the system boundaries for a longer time and further increases the throughflow and structure (exergy storage) in the system. As a result, specific entropy production decreases. • Exergy storage increases during ecosystem development (Jørgensen et al., 2000; Jørgensen, 2002; Fath et al., 2001). • Increased performance within the system by qualitative growth in system behavior from exploitative patterns to more conservative patterns, which are more energetically efficient
  17. 17. DLE effectiveness: Smartness • Smartness as DLEs effectivity to be adaptive to dynamic changes – monitoring and reorganising, closing services and resources that do not get agent attention are not fit to their needs; predicting agent behaviors that are fit to future DLE states and boosting up new relevant services (but these in turn require energy to be spent). • Smart specialization – creating within a socio-technical regime and sustaining by design niches for special fitness (such as adaptiveness ecological energy crisis, human-machine society) of certain kind of agents. • Giovanella, C. (2014) : The smartness or attractiveness of an ecosystem does not depend exclusively on its ability to run “all gears” in an effective and efficient manner. It, rather, depends on its ability to create an environment able to meet the individuals’ basic needs and keep them in a state of positive tension in which their skills are stimulated by adequate challenges, to favor the achievement of the self-realization (that is a Flow state) • Note. How can DLEs discover the potentially more stable, agent-environment fit future states and validate these? (monitoring chance seeking agents…) Which niches do the futuse states require? Which agents do future DLEs host? • Giovannella C. (2014). Smart Territory Analytics: toward a shared vision. In: SIS 2014, CUEC.
  18. 18. Case: School as a digital learning ecosystem • Based on digital learning services data from Ghanan, Georgian, Estonian internal and external DLEs cases were clustered to 3 types (assuming discovering DLE maturity types) • Structural components: digital infrastructure, computer class, mobile tools, digital resources • Transformative components: ICT training, ICT incentives, ICT support, ICT rules, ICT change management • Flow components: digital information management, networking among teachers, learning with ICT, analytics with ICT Cluster 1 – 12 cases, Cluster 2 – 27 cases and Cluster 3 - 13 cases
  19. 19. Case: School as a digital learning ecosystem • Type I. Digitally starting but still mostly non-digital • Applying transforming components with low connectivity to other DLE components, ICT training is the main transformative process that has impact in DLE on and is influenced by Digital information management and Learning with ICT in schools, as well as on Digital Infrastructure of schools. Transforming components Flow componentsStructural components Boundary growth brings the input of low-entropy material ( training) into the system.
  20. 20. Case: School as a digital learning ecosystem • Type II. Between not digital-digital successive states. • Low level of transforming components, low level of flow components, more interconnected DLE components. Transforming components Flow componentsStructural components Network growth is growth in connectivity of the system through additional energy–matter transactions and internal organization of the system. The energy is given off by breaking down the high quality, low-entropy energy (orderliness) for both growth and maintenance of the system.
  21. 21. Case: School as a digital learning ecosystem • Type III. Digitally transformed. • High level of structural components and flow components is achieved with moderate level of transformation components that are well connected with both. Transforming components Flow componentsStructural components Increased performance within the system by qualitative growth in system behavior from exploitative patterns to more conservative patterns, which are more energetically efficient
  22. 22. To do: Ecologically informed metadesign in DLEs. • Metadesign is providing the DLE design elements that enable to lessen the degrees of freedom of agents’ behaviour • Meta-design requires data from DLEs. • Meta-design promotes constrained self-organization of DLEs.

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