Archive for the ‘digital learning ecosystems’ 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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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.

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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.

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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.

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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