Sustainability of smart digitally enhanced learning ecosystems

August 20, 2018
We have published an article that models sustainability issues in digitally enhanced schools. The digital innovation sustainability bottlenecks in schools are related with actuating the organizations as systems, that is influenced by ill-developed feedback loops of data-, learning- and information flows for the purpose of intra-organizational innovation driven transformation as well as for transforming the external socio-technical regime to make it more fit for the innovative schools’ needs.
Sustainability 2018, 10(8), 2672; doi:10.3390/su10082672
Smart, Digitally Enhanced Learning Ecosystems: Bottlenecks to Sustainability in Georgia
Eka Jeladze, Kai Pata

This paper stems from the need to identify the sustainability bottlenecks in schools’ digital transformation. We developed the conceptual model of the smart, digitally enhanced learning ecosystem to map transformation processes. We posit that the notion of sustainability is central to conceptualize learning ecosystems’ smartness.
The paper presents the mapping results of Georgian public schools’ data using the interviews from 62 schoolteachers, ICT managers, and school principles. The qualitative content analysis revealed that even the schools with comparative digital maturity level could not be considered as smart learning ecosystems that are transforming sustainably. The findings call for the design of technology integration in the school as a dynamic transformation that balances two sustainability intentions—to stabilize the current learning ecosystem with its present needs, while not compromising its pursuit to test out possible future states and development towards them.
We suggest schools build on the inclusion of different stakeholders in digital transformation; nourishing their resilience to ruptured situations; widening the development, testing, and uptake of digitally enhanced learning activities; weaving internal networks for sharing new practices; conducting outreach to change the socio-technical landscape; and developing feedback loops from learning, data, and information flows to manage the changes.

Factors Determining Digital Learning Ecosystem Smartness in Schools

April 27, 2018

It has taken a while to jointly prepare an empirical data-collection in three countries from different economic background together with my two PhD students James Quaicoe and Eka Jeladze.

Based on the observation grid to investigate schools as learning ecosystems the unified dataset was formed to answer the questions i was interested in how digital learning ecosystem functions according to the ecological principles.

The paper is now out at journal:


Open Society Technologies

April 12, 2018

Open society technologies

In the XXI Century societies are becoming more open – balancing between choices of top-to down and bottom up, individual and collective, passive responsibility and proactive entrepreneurship, exercising tolerance, inclusion and democracy.

Politico-legal dimension of open society tries to channel persons’ political agency and spreading power, moving the public from responsive to proactive behaviours and political activism. For example, Public Administrations – both in the national and in the local levels – are promoting citizen participation at community activities and civic initiative.

The open society would keep no secrets from itself in the public sense, as all are trusted with the knowledge of all. This brings us to the open data and personal data privacy issues – data and trust have became the commody of open societies. The eight most frequently stated public service values are: impartiality, legality, integrity, transparency, efficiency, equality, responsibility and justice.

Surveillance of people in the digital society is becoming an issue, especially that it was often established without public debate. Many new media technologies serve both as a tool for organizing public commons and as a tool for surveilling private lives. For example in China social credit’ system is applied that analyses internet shopping and social media use in order to blacklist ‘lazy’ or wasteful citizens and allow those who behave well to borrow money.

While exercising open politico-legal dimension of open society at technology level, the dilemmas of social justice have to be solved at algorithm levels run by technologies – for example how we can divide the public goods with social algorithms. The studies of Lee and Baykal ( 2017) indicate that even algorithms mathematically proven to be “fair” may not achieve “fair” social division from human perspectives, and furher studies in algorithmic transparency and accountability are needed among public. For algorithmic mediation to be fair, algorithms and their interfaces should account for social and altruistic behaviors that may be difficult to define in mathematical terms.

Governments can solve wiked dilemmas dividing social goods and bads (such as where to open a factory or mine) using algorithms, that locate stakeholders in a problem on a social network and calculate their benefits so that nonrival, heterogeneous benets for each other will be established (see Elliott & Golub, 2017).

Second rapidly increasing governing technology is nudging people. For example (Sunstein et al., 2017) found that people around the globe generally approve nudges for governmental information campaigns, mandatory information disclosure imposed by governments. For nudges about default rules disapproval is higher. Majorities disapprove subliminal advertising and mandatory choice. New governing behaviours are also related to digital nativeness of people – younger people are more likely than older people to approve of more intrusive interventions (such as manipulative messages and default rules). However, political attitudes were found to have only a modest effect on approval rates of nudges.

Socio-economic dimension of open society prompts for thinner state at service level and reducing state intervention, making individuals less dependent upon the state, mobilizing them and building on their responsive social entrepreneurship by increasing community self-help. There is need to give back to the public sector” the our collective potential for governing and valuing our own resources – as it was maintained in the tribal times for commons goods. Governing socio-economic dimension of open society requires managing the services ecosystem – providing public goods (education, parks, roads, public safety, sanitation, utilities, legal systems and national defense provided by sovereign governments); estabishing fair access to the commons/common goods – the shared resources which people manage by negotiating their own rules through social or customary traditions, norms and practices; empowering the social goods provided by social entrepreneurship; and critically examining end making use of the open goods accessible through business activities. At technology level we are talking about open service ecosystems where citizens can get services seemlessly from state as well as other providers. The discoverability, connectedness of such services are critical in order to be cost-effective and avoid overlaps but simultaneously being inclusive at service level, and not overlooking the needs of different small groups’ needs. For example, Estonia has set a target for providing country as a service.

Citizens and small and medium companies are increasingly willing to participate as they became conscious of their key impact in the public life. Citizen Science as a new research inclusive way for channelling social activism is to be scaled up in open societies, we need technologies and ways of letting the people to make say of what innovations we should fund in the society, and what is the impact of radical socio-technical changes to their life at global and local level.

Political freedoms and human rights are claimed to be the foundation of an open society. The socio-cultural dimension of open society exercises tolerance and democracy in interaction between the public sector activities and individual people’s voluntary activities and their self-development, responsible consumption and environmental responsibility, establishing cohesion and inclusion. Technology plays a key role to enable this citizen participation and exercising social justice, such as inclusiveness at data level used in future intelligent decision support systems and how we open the data for people. People in open society must have technologically enhanced ways of taking the critical frame of mind in the face of communal group think, and staying in the Filter Bubble.  


As a collaborative effort we are launching at Fall 2018 the new master programme Open Society Technologies (taught in English) in Tallin University. The programme will bring together the expertise from eGovernance, Human Computer Interaction, Social Innovation and Digital Technologies to open up the black box of how to develop an Open Digital Society. Our mission with Open Society Technologies curriculum is developing new professionals like open society system architects, analyst, software developers or development managers, gardeners of cyber-physical systems in open society, choice architects, community technologist, digital policy advisors in society, social entrepreneurs  etc. able of maintaining the dimensions of open society.

Further reading:

Principles and values of good governance. http://ec.europa.eu/esf/BlobServlet?docId=13956&langId=en

Building Public Trust: Ethics Measures in OECD Countries (2000) http://www.oecd.org/mena/governance/35527481.pdf

REAL BLACK MIRROR (2018). https://www.thesun.co.uk/news/5730910/china-social-credit-rating-blacklists-citizens/ 

Sheperd, S. (2015). Surveillance of digital life and the use of sousveillance as a response https://medium.com/@sam.shepherd/surveillance-of-digital-life-and-the-use-of-sousveillance-as-a-response-7b306cfdb6e8

Lee, M. Baykal, S. (2017). Algorithmic Mediation in Group Decisions:Fairness Perceptions of Algorithmically Mediated vs.Discussion-Based Social Division https://www.cs.cmu.edu/~mklee/materials/Publication/2017-CSCW-AlgorithmicMediation_Fairness.pdf

Elliot, M Golub, B. (2017) Network approach to public goods. http://www.people.fas.harvard.edu/~bgolub/papers/jmp.pdf

Fetherson et al. (2017). The Persuasive Nudge Power https://www.bcg.com/publications/2017/people-organization-operations-persuasive-power-digital-nudge.aspx

Sunstein C.S et al. (2017). Behavioral Insights All Over the World? Public Attitudes Toward Nudging in a Multi-Country Study https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2921217

Government by nudge http://bigthink.com/Mind-Matters/government-by-nudge-is-a-global-phenomenon

The nation that thrieved by nudging its people http://www.bbc.com/future/story/20180220-the-nation-that-thrived-by-nudging-its-population

Milard, J., Carpenter, G. (2014) Digital technology in social innovation. TEPSIE is a research project. http://www.transitsocialinnovation.eu/content/original/Book%20covers/Local%20PDFs/124%20TEPSIE%20synopsis%20digital%20technology%20in%20SI.pdf

Maiolini et al., (2016). Digital Technologies for Social Innovation: An Empirical Recognition on the New Enablers https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-27242016000400004

Kotka, T. (2016). Country as a service. https://e-estonia.com/country-as-a-service-estonias-new-model/

EGOVIS 2016. Book Electronic Government and the Information Systems Perspective  https://link.springer.com/book/10.1007%2F978-3-319-44159-7

EGIVIS 2017. Book Electronic Government and the Information Systems Perspective http://www.springer.com/gp/book/9783642151712

Digital democracy https://www.nesta.org.uk/report/digital-democracy-the-tools-transforming-political-engagement/


Artificial intelligence for social good https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf


Citizen science in schools

April 3, 2018

Esitlus Reaalkoolis kodanikuteadusest.





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

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

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


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

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

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:


  • 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.


  • 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.



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.



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.


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