Archive for the ‘museum research’ Category

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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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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