Fundamental Characteristics of Formal Virtual Learning Communities

8/14/2019 Fundamental Characteristics of Formal Virtual Learning Communities

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Richard Schwier - Ben Daniel - Heather RossVLC Lab

Fundamental Characteristics of Formal Virtual Learning Communities


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Program of Research

3 years - 2004-2007 Purpose: develop a model of virtual learning

communities (VLCs) that is grounded in practice Do elements of terrestrial communities exhibit

themselves in virtual learning communities, and dothey inform our understanding of how thesecommunities contribute to learning environments?

How do contextual, pedagogical, social and cultural

issues influence participation in virtual learningcommunities?


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VLC model (Schwier, 2001)

Theoretical Underpinnings


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Theoretical Underpinning

Social capital (SC) in virtual learning communities(VLCs) is a web of positive or negative relationshipsamong learners

In the context of VLCs, SC serves as:

A framework for researchers to understand the flow of information and knowledge in a community A conduit for information and knowledge sharing among

learners An enabler of interactions that encourages peer-help and can

encourage lifelong learning and socialisation A lubricant for building trust, shared understanding and

collaboration among learners based on reciprocal actions


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Theoretical UnderpinningsTypical Indicators of SC

Interaction A mutual or reciprocal action between two or more agents determined by the number of messages sent and received

AttitudesPeoples general perception about each other and how such perceptions relate to interaction inthe community

Community typeThe type of the environment, tools, goals, and tasks that define the group

Shared understanding A mutual agreement/consensus between two or more agents about the meaning, orunderstanding of an object or each other

AwarenessKnowledge of people, tasks, or environment


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Theoretical UnderpinningsTypical Indicators of SC

Demographic awarenessKnowledge of an individuals demographic information (country of origin, language andlocation)

Professional cultural awarenessKnowledge of background training, skills and competences

Knowledge task awareness Knowledge about someones capability to perform a given task Capability awareness

Knowledge of peoples competences and skills in regards to a particular task

Norms and social protocolsThe mutually agreed upon acceptable and unacceptable behaviours in a community

Trust A particular level of probability an agent uses to assess the action of another agent


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Research Questions - Year 1

How do patterns of online interaction in thecourse inform our understanding of thecatalysts of community?

What is the nature of learning that emerged inthese formal learning environments Are the proposed features of community

manifest in online communication in formal virtual learning environments?


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Research Design

Two graduate level courses in year 1 One course included weekly structured online

discussions

One course included unstructured discussionrepository for team members involved inproblem-based learning course


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Data sources

Transcripts of online discussions Transcripts of email Interviews with selected participants

Online focus group

Adding in year 2: Sense of Community Index


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Analysis

*Structural Features Size and complexity of network Density and intensity of interactions

*Interactional Features Kinds of content exchanged in interactions The exchange flow or the directedness of the resulting interaction

*From Fahy, Crawford, & Ally (2001)

Community Features Evidence of social capital (trust, awareness) Observations about nature of learning Evidence of elements of VLC from model


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Data Analysis

Coding with Atlas ti Grounded theory approach for novel variables Purposeful coding for anticipated variables

(reliability estimates)

TAT & Sociograms of interactions (socialnetworking software and analysis in year 3)

Bayesian analysis (coming in year 2)


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A BBN is a graphical model for reasoning about probabilistic relationships among two or morevariables

Bayesian Belief Network Approach

Probability of an hypothesis, h, can be updated when evidence, e, has been obtained

Posterior

Prior

)()()/(

)/(e P

h P he P eh P =

Probability of Evidence

Likelihood

BayesTheorem


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Example of BBN Variables Mapping forSocial Capital

Nodes represent random SC variables with multivariate states and strengths


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BBN for Social Capital

Interaction Node set to Positive; p(i)=1.00


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Preliminary Results

Initial coding of approximately 80% of data from year 1

Reliability estimates yetto be calculated First attempts at

sociogram development


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Structural Analysis

Size & complexity of network 11 students, 1 instructor, 1 TA Rotating responsibility for moderation 1 discussion topic per week Moderator expected to post intro and manage discussion with

additional postings Students required to post initially, & expected to respond

freely Level of participation

Participation = 490 required postings/858 total postings = .57


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Patterns of Peripheral Interaction

From

To

S/R ratio

3.92 2.08 .91.90 .931.87 .67 .38.65.44 .641.22.55


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Patterns of

PeripheralInteraction:

Instructor&

Students


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Patterns of

PeripheralInteraction:

Instructor&

Students


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Patterns of

PeripheralInteractionamong

Students

Density = 2a/n(n-1) = .92


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Nature of Learning

Categories emerged from data Learning is multivariate and diverse within the

community - categories are tentative and share variance

Casual observation that there were significantdifferences between two versions of the course, and thecourse that emphasized online discussions andasynchronous events demonstrated qualitatively

different types of learning


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Preliminary results: Learning Sharing experiences the dominant expression of learning

The prominence of feedback was, to a large extent, builtinto the design of the discussions

Participants constantly provided feedback to each other There is a reasonable level of shared understanding,

argumentation, evaluation, elaboration, inquiry andreflection

Discussions are normally sustained for a certain periodof time (no measure of persistence yet)


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Incidence of elements & catalystsfrom model in transcripts

1 - Identity 6 - Historicity 2 - Future orientation1161 - Hospitality

1 - Autonomy 5 - Technology 8 - Plurality 0 - Mutuality 35 - Learning


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Future Track individual intensity/involvement over time. What are the characteristic elements of learning in

VLCs? What are effective learning strategies in VLCs? Is there any variation in the quality of discussions

generated through synchronous and asynchronousinteractions in VLCs?

How to develop a BBN model of learning in VLCs thatcan be used to understand the nature of learning in

VLCs?


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What? Topic: Online discussions

Who? Elizabeth Murphy, Memorial University

Why? Come prepared with a research questionre online asynchronous text-based discussion

When? April 8, 2005 11:00 a.m., MT

Where? Online at Elluminate Live!

Stay Tuned! CIDER Session III

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Fundamental Characteristics of Formal Virtual Learning Communities

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