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Survey instrument: digital workers’ preferences of informal learning opportunities in socio-technical learning ecosystem

January 12, 2017

The survey was developed based on the informal learning interactions in workplaces described by Ley and associates [2014]. The survey items elaborated possible socio-technical system functionalities using the ideas from the prototypes of Learning Layers tools. The online survey comprised of three groups of statements that represent the socio-technical learning system dimensions for informal learning at work:

  1. Sensemaking statements: Learn & organize knowledge (11), Share knowledge (5), Annotate knowledge (5)
  2. Scaffolding statements: Search Resource (3), Find Resource (2), Awareness of resources (5), Find expert (4), Share help requests (2), Get expert Guidance (6)
  3. Knowledge maturing statements: Accumulate knowledge in system (5), Co-construct knowlede (7), Validate resources and experts with technology means (5)

ANNEX. Survey: Socio-technical learning ecosystem opportunities for informal workplace learning

SENSEMAKING
Learn and Reorganize knowledge

  • I find it useful identifying learning needs at work using the computer support
  • I find it useful revisiting the exciting learning moments later on
  • I find it useful taking records (notes, memos, reminders, photos, videos etc.) to capture my learning moments at work
  • I find it useful that learning records captured at work could be used for further learning.
  • I find it useful organizing the records of my learning moments into meaningful learning episodes
  • I find it useful making records of which tools/resources I have used at work
  • I find it useful reflecting (writing, audiotaping etc.) about learning records to make sense of what was learned
  • I find it useful organizing records of learning into personal portfolio
  • I find it useful collecting into personal portfolio learning resources about interesting topics
  • I find it useful composing different views of records in portfolio for different purposes.
  • 5I find it useful learning from videos of good practice and failure created by others

Annotate knowledge

  • I find it useful adding keywords/notes to my learning records
  • I find it useful organizing learning records/resources with tags/keywords suggested by the system

Share knowledge resources

  • I find it useful that my reflections about learning will become part of shared resources
  • I find it useful that author can decide the access and sharing rights for each record in the personal portfolio
  • I find it useful that each document could be shared with others for learning purposes
  • I find it useful sharing documents/folders with other professionals for learning
  • I find it useful sharing documents with other professionals across workplaces

SCAFFOLDING
Search knowledge resources

  • I find it useful searching the latest information about the topics of my learning interests
  • I find it useful using mobile devices for searching learning materials directly at work
  • I find it useful searching suitable learning materials from the shared system

Find knowledge resources

  • I find it useful finding learning materials related to my work easily during working
  • I find it useful to access my previous learning records when I need them during work

Awareness and recommending

  • I find it useful to get automatically notices about shared resources and learning activities of other professionals in my field
  • I find it useful to get automatical notices about the modifications of certain normatives or guidelines
  • I find it useful discovering new learning interests by getting notifications of learning interests and needs of others
  • I find it useful getting system suggestions of the most relevant learning materials that other users have considered useful
  • I find it useful using guidance materials created by other learners

Find expert

  • I find it useful expanding social networks with new experts
  • I find it useful of requesting help from my social network at work
  • I find it useful identifying trustful learning experts by their rank of the quality of help they have provided
  • I find it useful getting suggestions to expand my social network with relevant experts who can provide guidance

Get expert guidance

  • I find it useful negotiating problem/task context while receiving/providing guidance
  • I find it useful getting less guidance when competence increases
  • I find it useful mainly receiving guidance how to better organize my learning activities at work
  • I find it useful mainly receiving hints how to make sense of new knowledge in work context
  • I find it useful being guided by experts in using collective resources
  • I find it useful being guided by experts in using the objects and tools at work

Share help requests

  • I find it useful seeing the help requests from others that match with my expertise
  • I find it useful sharing the help requests in my social network to locate most relevant experts

KNOWLEDGE MATURING

Co-construct knowledge

  • I find it useful co-constructing new learning resources from different people’s contributions
  • I find it useful that learning resources can be improved by incorporating different viewpoints from experts
  • I find it useful that learning resources can be improved by integrating related resources
  • I find it useful improving official descriptions of work processes, normatives and guidelines by local networks of experts
  • I find it useful discussing normatives and guidelines locally among experts
  • I find it useful creating knowledge of work processes as a result of many contributors‘ efforts
  • I find it useful collecting knowledge of good guidance and support from actual guiding practices at workplaces

Validate with technology means

  • I find it useful that other professionals in the network can rate learning resources
  • I find it useful that other professionals in the network can endorse my competences
  • I find it useful endorsing personal expertise by networking peers
  • I find it useful rating or commenting learning materials from my task context to make them better contextualized
  • I find it useful rating experts based on provided guidance

Accumulate knowledge

  • I find it useful that everyone’s learning events can be automatically traced
  • I find it useful that each tool and learning material has digital records and use-histories.
  • I find it useful that digital documents would capture discussions about learning episodes around them
  • I find it useful that learning resources can collect discussions about them
  • I find it useful that learning resources can be improved by accumulating their use-histories
  • I find it useful that normative guidelines at work would consist of ‚official‘ immutable and ‚inofficial‘ mutable content
  • I find it useful influencing the collective knowledge by personal notes
  • I find it useful accessing the use-histories of objects, tools or digital learning resources
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Smartness and innovativeness of learning ecosystems

November 9, 2016

Last ICWL 2016 conference in Rome made me reconsider the innovative learning ecosystem concept in my studies and instead consider using the smartness of learning ecosystems since innovative is a relative concept while smartness is not, as well as smartness may be nicely interpreted as a niche providing fitness and flow experiences.

I liked an interesting keynote by Carlo Giovanella from Tor Vergata University of Rome – Dept. of Educational Science and Technologies. He described a survey done in several universities to capture the smartness of educational learning ecosystems – Smartness of learning ecosystems and its bottom-up emergence in six european campuses (2016): Survey with university students at different campuses: a) the detection of the degree of satisfaction related to the levels of the Maslow’s Pyramid of needs, and b) the detection of indicators related with the achievement of the state of “flow” by the actors involved in the learning processes. Identifing: a) the set of the most relevant indicators; b) a “smartness” axis in the plan of the first two principal components derived by applying a Principal Component Analysis (PCA) to the spaces of the selected indicators.

He refers to smartness as follows:

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 (Giovanella, 2014)  –

Giovannella C., Smart Territory Analytics: toward a shared vision. In: SIS 2014, CUEC, (2014).

NOTE: that actually is the definition of the niche in ecology, but Giovanella in 2016 article combines the Flow state as the required quality of satisfaction for people in this learning nichestate where challenges are exciting and adequate to the skills owned by the individuals, which, in turn, are expected to be improved due to the challenges.

In his previous paper of smart cities Giovanella defines smartness of cities as follows: a city is smart “when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance“.

This captures the systemic, organizational view to smartness and incorporates implicitly bottom-up self-organization in an ecosystem, and explicitly sustainability of the learning ecosystem as a common good and high quality of individual’s life as the evaluation criteria.

——————–

Giovanella’s approach technically was very similar what we have done in studying the school learning ecosystem services in Georgian, Ghanan schools (see below). However, we used observation and interviews (the external view to the existing niches).  We mapped data on the digital service grid quantitatively as an input. So we yet cannot measure the ecosystem fit to user’s challenges as the quality of smartness but rather we may set learning type variables such as learning and facilitation services related with classical ICT teaching or innovative ICT teaching and see how the other ecosystem services determine those.

———————-

Georgian papers:

Jeladze, Eka; Pata, Kai (2016). Digitally Enhanced Schools and Service-based Learning Ecosystem. EDULEARN16 Proceedings: 8th Annual International Conference on Education and New Learning Technologies. Barcelona (Spain), 4-6th July, 2016. IATED, 1569−1578.

K-means cluster analysis was run and 2 models of schools were identified using developed instrument. Discriminant analysis was run to identify predictor variables for further analysis of the schools’ belonging to certain model. Innovative and non-innovative schools differed by teacher-student partnership, authentic and flexible learning environment, but the biggest difference was in change management domain.Discriminant analysis detected following variables as predictors: school’s ICT vision and agenda, motivation and support system promoting innovative practices, teachers’ professional learning relevance to the curriculum requirements and school strategy.

Eka Jeladze and Kai Pata (2016). Technology Investment and Transformation Efforts in the Public Schools of Georgia (2016) ICWL 2016

Beyond the previous study we built Bayesian Dependency model for innovative schools’ cluster to find probabilistic dependencies of the services in digitally enhanced schools illustrated the model with qualitative case study descriptions. The findings suggested that trade-off type of services requiring schools initiative to get service and change management services were the biggest determinants of the schools belonging to the innovative technology-enhanced learning ecosystem type.

Ghanan papers:

Quaicoe, James Sunney; Kai, Pata; Jeladze, Eka (2016). Digital Learning Ecosystem Services and Educational Change in Ghana’s Basic Schools. EDULEARN 16 : 8th Annual International Conference on Education and New Learning Technologies. Barcelona(Spain) 4th to 6th July 2016. Ed. L. Gómez Chova, A. López Martinez, & I. Candel Torres. iated, 4887−4895. (EDULEARN 16 Proceedings).

This paper mapped descriptively Internal, External and Transactional Infrastructure, Learning and teaching and Change management services in Ghana and revealed the developed and undeveloped service areas for Ghanan schools and the mismatch between externally provided and internally applied services.

Quaicoe,James Sunney; Pata, Kai (2016). Digital Divide in Learning Services in Ghana’s Basic School. Advances in Web-Based Learning – ICWL 2016: International Conference on Web-based Learning – ICWL 2016 in Rome, Italy, 26-29 October 2016.. Ed. M. Spaniol, M. Temperini, D.K.W. Chiu, I. Marenzi, U. Nanni. Spring: Springer International Publishing, 83−88.

The results of Canonical Discriminant function analysis indicated that external digital learning services informed digital divide in two school clusters – the less advanced schools were not able to proactively transact external digital learning services into their schools.

———————–

Since our grid data contain many services, the system view to services’ interaction appears to be complex. We have reduced services to the following domains:

Innovative ICT learning
Classical ICT learning in computer class and lessons, factual learning
Centrally provided technology, connectivity and resources
Transactionally obtained technology, connectivity and resources
Norms and ownership of ICT related aspects
Training and professional learning for ICT
Open access to resources
Resources provided by external business
Maintenance, Security and monitoring
Incentives and motivation
Peer-learning, networking, sharing resources
Satisfied access to ICT and teaching competences
Collective Involvement to change management if ICT in organization
Authoritive ICT development in organization

Linear modelling with stepwise method with united dataset from Ghana and Georgia indicated school learning ecosystem factors that determine certain ICT learning to be prevailing in schools:

  • the predictors of classical ICT teaching in school learning ecosystem are the availability of services from types: Incentives and motivation, Authoritive ICT development in organization, Open access to resources
  • the predictors of innovative ICT teaching in school are the availability of services from types: Peer-learning, networking, sharing resources, Transactionally obtained technology, connectivity and resources, and Open access to resources
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Citizen science and public engagement perspectives

October 3, 2016

I attended in Oslo Science Park the seminar on citizen participation, expertise, and knowledge sharing in cultural heritage archives and natural history institutions. The seminar dealt with different aspects, which i found interesting to keep a record about.

Bernard Schiele from University of Quebec emphasised the need to overcome the Deficit model in explaining the role of public engagement and citizen science – a knowledge gap between public being illiterate about science and scientists seen merely as the teachers about science. He opened up the high level bodies expectations recently prevalent about public engagement in science as Triple democratisation and engagement model – i) including laypersons in the phase of making science decisions while exercising democratic rights, ii) making science as a co-production involving knowledge of laypersons, as well as iii) making reports accessible to laypersons in an understandable format.

Socientize.eu White paper on Citizen Science aims to support policy makers on European, national and regional level when setting up future strategies of civic engagement in the excellence in science

  • Interrelation in deficit model is paradigm asymmetric, in public engagement model symmetric.
  • Interpersonal relationships in deficit model are compelling, while in engagement model collaborative.
  • Interaction in deficit model positions science into an authoritative position, while in engagement model equal rights to contribute are considered.
  • The conditions in deficit model between scientists and laypersons are following the dependence model regarding the latter, in engagement model autonomy of science and laypersons is practiced.
  • In dependence model the behaviour of laypersons is submissive, while in engagement model the reciprocal engagement is practiced.
  • The knowledge transfer changes fro deficit model one-directional transfer from science to people towards mutual knowledge transfers.

Three models for participation and public engagement in science are: 1) dialogue critical (such as exhibitions, fairs), ii) deliberative democracy (such as consensus conferences) and iii) knowledge co-production (participating together in research projects operating from different locations).

In the discussion the interesting books of social ecology were mentioned: Social Ecology and Social Change by Erik Eiglad.

Science museums so far have been presenting science already made but it should be shifted towards engaging people into science in making what happens at this moment, time and space. The changes that have to be achieved are:

  • From one voice towards multiplicity of voices
  • From dominant view to various views
  • From presenting truth to presenting conflicts, disagreements
  • From linear approach to questions and challenges, multi-faceted open science approach that involves laypersons in engagement
  • From facts, results to relations between people
  • From closed, stabilised, fixed, secure knowledge to presenting also tentative results, failures, aberrations, presenting unfinished knowledge and processes

New media has opened new forms of participatory public engagement that has to be:

  • reciprocal regarding exchanges of knowledge between layperson and scientists
  • regarding the interdependency of different society groups and scientists
  • allowing local knowledge in context to impact science and technology

Dick Kasperowski from LET studio in Dept. of Theory of Science, University of Gothenburg introduced a meta-study considering citizen science papers. He divided citizen science initiatives into two types: i) Perception mode, and ii) Representation mode

However, if to look what way science museums, knowledge institutions and archives so far engage with public, the tendency of engagement is towards using the laypersons as a work labour, either in data collection, metadata tagging, transcribing, validation (having layers of validation for verification of contributed amateur data) or as sensors in pattern recognition (Perception mode). That is not a democracy in open citizen science, rather Taylorism 2.0.

Issues: The scaling up of data done with amateurs is still a problem – the data collected by them are not considered as same valuable. It has been found that when amateurs use protocols, these practices do not scale – protocols do not withstand many users. Protocols start to leak – amateurs start to do other things than expected.

What is needed – instead of mobilising human perception the interpretational cultural contributions should be requested from amateurs. Cultural contributions could allow creating values beyond tasks.

It is important to move from citizens as research object to citizens as research subject.

European citizen science association claims that people should be included also in hypothesis creation and interpretation.

Interesting examples of citizen science:

galaxyzoo

  • Shakespeare’s world – Transcribe handwritten documents by Shakespeare’s contemporaries and help us understand his life and times. Along the way you’ll find words that have yet to be recorded in the authoritative Oxford English Dictionary, and which will eventually be added to this important resource.

shakespeareworld

  • Micropast: crowd sourcing: You can assist existing research projects with tasks that need human intelligence, such as the accurate location of artefact findspots or photographed scenes, the identification of subject matter in historic archives, the masking of photos meant for 3D modelling, or the transcription of letters and catalogues. Other tasks might require on-location contributions by members of the public, such as submitting your own photographs of particular archaeological sites or objects.

micropast

arecheology

Read more at: http://hyperallergic.com/122740/crowdsourcing-the-bronze-age-in-a-new-platform-for-archaeology/

Help scientists recover Arctic and worldwide weather observations made by United States ships since the mid-19th century by transcribing ships’ logs. These transcriptions will contribute to climate model projections and will improve our knowledge of past environmental conditions. Historians will use your work to track past ship movements and tell the stories of the people on board.

oldweather

zooniverse

List of citizen science projects from wikipedia

One form that citizen science is taking is citizens using the data they collect in law cases ( such as plant data, water or air data).

Example Louisiana bucket brigades

Alexandra Everleigh presented motivational issues in citizen participation projects:

Issues: small number of participants do the most work, enthusiasm drops in time, contributing very much on their own terms

The contributions are motivated by fun, wish to contribute in science, unhealthy addiction, feeling part of something bigger or part of community ( social exclusion issues)

Motivational elements used to engage people more are: competitive participatory levels, badges, race to the finish (that is discouraging if the target is far). Best motivations would be personal challenges that are motivated from own interest, demonstrating how contributions make the difference; finding own narratives; open-ended discoveries.

Example of other engagement forms: NYPL Emoji Bot twitter account: Send  an emoji, receive an image  from  collections.

emoji-bot

Christine Hine, University of Surrey, Dept. of Sociology has recently edited special issues on socio-technical systems in Science and Technology Studies 29(1)-29(4), where several public engagement projects are depicted.

Issue 1: http://ojs.tsv.fi/index.php/sts/issue/view/3902

Issue 2: http://ojs.tsv.fi/index.php/sts/issue/view/3926

  • Building Knowledge Infrastructures for Empowerment: A Study of Grassroots Water Monitoring Networks in the Marcellus Shale Kirk Jalbert

This paper characterizes the activities of two nongovernmental environmental monitoring networks working to protect watersheds in the Northeast United States from the impacts of shale oil and gas extraction. The first is a grassroots coalition of advocacy groups. The second is a large network managed by academic institutions. In both cases, knowledge infrastructures were built to distribute resources and to assist members in using data to make scientific claims.

Issue 3: http://ojs.tsv.fi/index.php/sts/issue/view/4160

  • Co-Observing the Weather, Co-Predicting the Climate: Human Factors in Building Infrastructures for Crowdsourced Data Yu-Wei Lin, Jo Bates, Paula Goodale

We found that conducting citizen science is highly emotional and experiential, but these individual experiences and feelings tend to get lost or become invisible when user-contributed data are aggregated and integrated into a big data infrastructure. While new meanings can be extracted from big data sets, the loss of individual emotional and practical elements denotes the loss of data provenance and the marginalisation of individual ef orts, motivations, and local politics, which might lead to disengaged participants, and unsustainable communities of citizen scientists. The challenges of constructing a data infrastructure for crowdsourced data therefore lie in the management of both technical and social issues which are local as well as global.

An interesting SEAD network

http://xsead.cmu.edu/sets/23

 

 

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Coherence and consistency in ecological learning

December 22, 2015

I have found an interesting PhD thesis by Jornet, Alfredo(2014)

https://www.duo.uio.no/bitstream/handle/10852/43305/PhD-Jornet-DUO.pdf?sequence=4&isAllowed=y

that reminded me my ideas of conceptual coherence and consistency in one paper that never was published. There in 2006  i wrote about conceptual coherence the following:

The definitions of conceptual coherence often combine the cohesiveness and consistency properties of conceptual knowledge. Coherence is a definition that is applicable for characterizing the states of the elements of some larger units (eg. phenomenological primitives, epistemological resources). Coherence is also related to the contextual and time-related dimensions. Cohesiveness is the property characterizing the conservation of inherent relationships among ideas in one explanation framework or the links among several related conceptual frameworks. Consistency is defined as a property indicating that students’ explanations of a certain phenomenon are stable, independently of the variable contexts that depend on the viewpoint of the explanation. It means that students are able of activating same locally coherent sets of ideas again and again in time, independent of task contexts.

That study in 2006 was conducted under the cognitivist (representational mental model) framework that i abandoned in next years being fascinated of distributed cognition and ecological learning models.

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This PhD study looks coherence and continuity in the context of embodied and distributed cognition. It uses coherence and continuity to address the sense-making practices by means of which relations of signification are established within and across contexts and situations. Coherence denotes the achievement of order, whether within or across a given problematic or situation. Continuity refers more explicitly to the achievement of coherence across settings and activities, which has been traditionally investigated as the question of transfer.

The coherence and continuity of any set of ideas or concepts, as made relevant by the participants during joint activity, cannot be analyzed in terms of a priori formal properties of either the material setting (e.g., texts, graphs, demonstrations) or the individuals’ thinking (e.g., a learner’s mental representations of texts, graphs, or demonstrations), but must be treated as the result of material and practical operations that involve both.

The thesis suggests that several studies (bodily episodic feelings, that is, a bodily and context-bound sense of “having-been-there” (p. 311), it is only as part of addressing and being addressed by others during conversation that the initial connection comes to be developed as a conceptual one – Nemirovsky, 2011; context-sensitive concept projections and the transfer-in- pieces framework– diSessa & Wagner, 2005; Wagner, 2010) refer to the an expansion of their conceptualizations of learning beyond the individual mental abstract representation to better account for the intrinsic relation between subjects and their immediate material and social environments. A concept projection is “a set of knowledge elements with which a knower assimilates and interprets … the situation’s affordances in a particular, meaningful way” (Wagner, 2010, p. 450).”

Some interesting findings from this PhD study:

  • an initial sense of similarity motivates action that transforms the situation, which in turn allows for the eventual achievement of a new conceptual way of accounting for a new existing order.
  • Inference, as a cognitive process, does not precede, but rather is the outcome of, a larger unit of activity. 
  • Individuals constitute and are constituted by the establishment of conceptual coherence because they are subject to the objective changes that bodily activity brings about in their attunement to the accountable, collective organization of activity
  • Any assumption about what particular actions, utterances, artifacts, and representations “mean” as cultural tools needs to be set aside and instead requires taking a first-time-through perspective of the participants

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Reading this PhD study and my old research made me think of my other thoughts about the formation of cultural patterns as niches, that may be described using both of these concepts – coherence and the consistency.

I think coherence and consistency are important both in the circles of personal pattern formation and stabilisation, as well as cultural pattern stabilisation, since both are formed as niches from instances of experiencing. So coherence in pattern or meaning niches requires to be formed across different contexts. How do these instances of experiences align themselves into the coherent pattern or meaning that we can perceive? Is it the distributed nature of those different context experiences that some ways form a consistent network like in the connectionist models? On the other hand how from the contextual coherence point of view do individuals activate cultural patterns and align them with own experienced patterns?

In my old paper i refer: “Hammer et al. (2004) used the term “framing“ to explain the activation of a locally coherent set of epistemological resources, which in the moment at hand would be activated in a mutually consistent and reinforcing way. Framing presumes distributed encoding among resources rather than accepting the notion that knowledge is located in any particular cognitive resource. Distributed encoding is the distributed interpretation across a network of cognitive elements, while the frames can often shift easily.

The consistency of patterns and meanings suffers from time delay and the bubble effect. So from the ecological learning point of view it is personally rather not useful to create consistent cognitive patterns but keeping them open to chance events that can destabilise them from coherency. I wonder how many recommender systems focus of destabilisation processes rather than stabilisation ones.

 

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Social positioning in hybrid social learning networks (HSLN)

October 23, 2015

Our paper of social recognition provision practices in professional help seeking forums led us towards thinking how to improve the knowledge building within such socio-technical systems.

John Cook has suggested in the Learning Layers project a concept of Hybrid Social Learning Networks (HSLN). “Hybrid Social Learning Networks (HSLNs) is a concept describing socio-technical systems that enable Zones of Possibility (ZoP) to emerge when people and artifacts interact and engage in social positioning practices while learning in informal workplace learning situations. In a Zone of Possibility people connect and interact through a hybrid network of physical and technology-mediated encounters to co-construct knowledge and effectively engage in positioning practices necessary for their work.  ”

“Cook: The Zone of Possibility definition thus makes a distinction between the Zone as the structure (a hybrid network of physical and technology-mediated encounters blending socio-technical systems and the actual practice), and the behaviours that the Zone allows (calls for) (connect and interact, to co-construct knowledge, calls for orchestrating social supports – navigation and bridging aids, social positioning, positioning practices necessary for their work), and the resulting functions that the Zone takes as the Possibility (that learners can benefit from the ideas of others). ”

In HSLN-s the different problems and issues can be discussed in specific targeted work groups, that may allow knowledge to be maturing through knowledge building practices. These working groups embedded to wider HSLN can be considered working as in the Zone of Possibility for workplace learning.

Part of what happens in working group relates with dynamic social positioning and identity creation.

I have modelled  how the HSLN and the working group may be interrelated.

HSLN

Figure: social positioning and identity in the knowledge building workgroups of HSLN.

I used the communication acts’ model we presented earlier, to indicate in timeline how the group who is embedded in HSLN may be influenced by these communication acts during the cycles of knowledge maturing.

The initial group that works for problem sends out requests for, and receives recommendations that are based on socio-technically aided validations that contribute to accumulating credibility to people and resources. Knowledege maturing in the group as well as the social positioning in the group is advancing due to these added resources and persons, and the collaborative knowledge-building the group does. The accumulated credibility from the HSLN contributes to social positioning in group as follows: it may give expertise based ranking among the group members to certain people in group depending of a certain time moment, so social position may change dynamically due what happens with involved persons – which resources they bring into the group (credibility of resources enables calculating persons topic related credibility), what is their personal credibility based on these resources, plus what is their overall credibility in HSLN). Additionally, within the group the adequacy of credible resources and credible persons in respect to topic in hand is estimated as a whole, and if group expertise is low, the recommendations could be pushed by HSLN to add relevant credible persons and resources.

Social ranking of persons within group in time moment may suggests identity and roles in teams, such as leader expert (responsible for summarizing, setting rules to how document is created), and experts who provide arguments (responsible for introducing alternatives, validating).

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Distributed cognition model at workplace learning situations

September 2, 2015

Finally some parts of the empirical data from Learning Layers  http://learning-layers.eu/ project have been mapped back to the distributed cognition model.

This work relates cultural pattern appropriation in learning .

Slide1

This figure describes how patterns “Problem solutions”, “Standards” or “Guidelines” may be updated and what is the role of scaffolding and knowledge maturing practice elements in workplace problem-solving and learning process.

The explanation of the model (draft so far):

Formation and stabilization of “Problem Solutions” or “New guidelines” or “Standards and normatives” as patterns:

1) The individuals, groups and collectives may initiate the Request for help on the basis of Dissonance between own knowledge and that knowledge that supposedly collectively possessed, or what the group might co-create collaboratively. The triggers for Requests for help are urgent problems, mismatch between existing guidelines/normatives/standards and the problem situations, or missing guidelines/standards/normatives for novel problem situations.  The Request for help present to the selected expert, group of workmates or the collective (network, special group) either the “Problem and some possible solutions (with evidences)”; the “Guideline/Standard/Normative (with new non-corresponding evidences)”; or the “Actual work process with a novel project (with the access to monitor/participate in it)” .

2) The targeted helpers are selected based on proximity and trust. When the Request for help is directed towards individual experts, selected expert groups with different expertise, or groups with relatively equal and incomplete expertise, the specific short-term or long-term workforces are created. In case of sending help requests to the collectives representing self-organised members (e.g. special groups, networks), the individuals in the collective may be sufficiently alert having Awareness of upcoming Requests for help – they have collectively taken responsibility for providing help when relevant. Helping practice in collective level is most often an informal activity, while helping practice in collaborative level by experts and groups may be formally embedded to the actual working practices.

3) The individuals in the collective use Negotiation/Grounding for specifying the problem-solution/guideline/standard/normative. The end of Negotiating/Grounding is establishing the common ground. This common ground about problem-solution is often not Formalised/Standardized and shared with the collective, thus it will not be accessible to others for future Uptake. When the individuals of the group Re-experience the problem-solution they continue generating new variations and the pattern is not easily formed. The common ground established for updating or developing new guideline/standard/normative is usually Formalised/ Standardized, shared at collective level, and becomes accessible for further Uptake/Re-experiencing what can amplify the pattern formation.

4) The individuals involved in helping usually Contextualise the problem/ guideline/standard/normative into their locations or situations enabling the variety of alternatives to be discovered in short time. The de-contextualization to generalize solution/guideline/standard/normative is supported in work-groups who discuss or can practice something together by Co-constructing the shared documents that mediate the Formalization/Standardization and are more useful for later pattern sharing at the collective level.

5) Different forms of Validation are used in the Negotiation/Grounding process. The individuals or groups share examples of problem-solution/guidelines/normatives/standards, Validating those with evidences (photos, schemas), personal expertise gained in practice, guidelines/normatives/standards and real examples of practicing to try something out.

The members of the groups and collectives also gain Social Recognition that validates them as experts.

6) The Formalization/Standardization of solutions/guidelines/standards/normatives happens mostly at the collaborative level Co-Creation activities. The collective groups (such as special groups in What’s App) or groups formed at workplace for solving urgent problems do not Formalize/Standardize the solutions/guidelines/standards/normatives that hinders pattern amplification through Uptake/Re-Experiencing, but leaves room for different alternative variations to be used.

7) Persons, groups and collectives may recommend personally or collectively Validated and/or Formalized/Standardized problem-solutions/guidelines/standards/normatives as patterns to resolve help requests [alternatively the Recommender systems may select based on the Request for help from the existing solutions in the collective knowledge base the most relevant “Problem Solutions” or Experts and recommend those]

Change of “New Problem solutions”, “New guidelines” or “New Standards and normatives” as patterns:

1) The practitioners at work belong to the formal/informal groups and collectives (e.g. networks or special interest groups), share common knowledge and practices, and have Awareness of mutually interesting problem-solutions/guidelines/standards/normatives. This Awareness may be mediated by some technologies such as forums (WhatsApp) and databases to discover guidelines/normatives/standards.

2) The practitioners (both individuals and representatives of groups and collectives) perceive at work situations Dissonance between the solutions/guidelines/standards/normatives they know and have experienced, and the potentially existing patterns (solutions/guidelines/standards/normatives) in their formal/informal groups and collectives. They also may discover the mismatch in solutions/guidelines/standards/normatives and actual needs.

3) They involve other experts through Requesting for help from individuals, groups and collectives to Co-create new solutions/guidelines/standards/normatives or will develop by the new solution or practice themselves. Alternatively they Create/Construct novel solutions, and Re-experience to test them out.

They share the New Solution Validating it with evidences with the groups or collectives, and Request for Validation.

4) The addressed expert, group or collective is Negotiating/Grounding with the proposer to specify the New Solution or Practice.

5) As part of Negotiating/Grounding the expert, group or collective is Validating the New Solution by comparing it with existing solutions/guidelines/standards/normatives as well as with expertise, personal experiences, similar cases or with the commonly accepted collective practice. They also aim at achieving common ground about the Formalization/Standardization of it. As a result they may decide the New Solution or Practice to be significantly different and useful and yet missing; or find it being the instance of some existing solution/guideline/standard/normative.

Possible Validation in action may take place to develop the new solution/guideline/standard/normative, that incorporates Contextualization of New Problem Solution to be tried out in at different situations or de-contextualization in a specific case in which the members use the mediating shared document to Formalize/Standardize the New Problem Solution. The Formalization/Standardization makes it shareable and other practitioners can Uptake/Re-experience it that amplifies the new pattern in the community of professionals.

Application of “Problem Solutions”, “New guidelines” or “Standards and normatives” as Patterns

1) At some point the individuals/groups or collectives Re-contextualise the provided solutions and applies guidelines/standards/normatives to solve the problem in hand; they may Re-Experience the solutions/guidelines/standards/normatives several times until Uptake happens and it becomes frequently/commonly used. That will strengthen the pattern.

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Book: Mass collaboration in education is soon out

July 7, 2015

Mass Collaboration and Education

Cress, Ulrike; Moskaliuk, Johannes; and Jeong, Heisawn (Eds.)

Table of Contents

Introduction

Chapter 1: Ulrike Cress; Heisawn Jeong; and Johannes Moskaliuk. Mass Collaboration as an Emerging Paradigm for Education? Theories, Cases, and Research Methods

Part I: Theoretical Approaches to Mass collaboration

Chapter 2: Allan Collins. A Brief History of Mass Collaboration: How Innovations over Time have Enabled People to Work Together More Effectively

Chapter 3: Gerhard Fischer. Exploring, Understanding, and Designing Innovative Socio-Technical Environments for Fostering and Supporting Mass Collaboration

Chapter 4: Mark Elliott. Stigmergic Collaboration: A framework for understanding and designing mass collaboration

Chapter 5: Ulrike Cress; Insa Feinkohl; Jens Jirschitzka; and Joachim Kimmerle. Mass Collaboration as Co-Evolution of Cognitive and Social Systems

Chapter 6: Aileen Oeberst; Joachim Kimmerle; and Ulrike Cress. What is Knowledge? Who Creates it? Who Possesses it? The Need for Novel Answers to Old Questions

Chapter 7: Wai-Tat Fu. From Distributed Cognition to Collective Intelligence: Supporting Cognitive Search to Facilitate Online Massive Collaboration

Part II: Cases of Mass Collaboration

Chapter 8: Tobias Ley; Paul Seitlinger; and Kai Pata. Patterns of Meaning in a Cognitive Ecosystem: Modeling Stabilization and Enculturation in Social Tagging Systems

Chapter 9: Aileen Oeberst; Ulrike Cress; Mitja Back; and Stefen Nestler. Individual versus Collaborative Information Processing: The Case of Biases in Wikipedia

Chapter 10: R. Benjamin Shapiro. Toward Participatory Discovery Networks: A Critique of Current Mass Collaboration Environments and A Possible Learning-Rich Future

Chapter 11: Deborah A. Fields; Yasmin B. Kafai; and Michael T. Giang. Coding by Choice: A Transitional Analysis of Social Participation Patterns and Programming Contributions in the Online Scratch Community

Chapter 12: Ricarose Roque; Natalie Rusk; and Mitchel Resnick. Supporting Diverse and Creative Collaboration in the Scratch Online Community

Chapter 13: Brigid Barron; Caitlin K. Martin; Véronique Mertl; and Mohamed Yassine. Citizen Science: Connecting to Nature through Networks

Chapter 14: Sabrina C. Eimler; German Neubaum; Marc Mannsfeld; and Nicole C. Krämer. Altogether now! Mass and Small Group Collaboration in (Open) Online Courses – A Case Study

Chapter 15: Thomas Herrmann. Socio-Technical Procedures of Facilitated Mass Collaboration for Creative E-Participation

Part III: Methods to Empirically Analyze Processes of Mass Collaboration

Chapter 16: Iassen Halatchliyski. Theoretical and Empirical Analysis of Networked Knowledge

Chapter 17: H. Ulrich Hoppe; Andreas Harrer; Tilman Göhnert; and Tobias Hecking. Applying Network Models and Network Analysis Techniques to the Study of Online Communities

Chapter 18: Ivan Habernal; Johannes Daxenberger; and Iryna Gurevych. Mass Collaboration on the Web: Textual Content Analysis by Means of Natural Language Processing

Chapter 19: Olga Slivko; Michael Kummer; and Marianne Saam. Identification of Causal Effects in the Context of Mass Collaboration