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Record W2291791987 · doi:10.82308/48494

Learning in an informal web-based community of practice : a study of community, interpersonal, and individual planes

2008· article· en· W2291791987 on OpenAlexafffund
Gyeong Mi Heo

Bibliographic record

VenueeScholarship@McGill (McGill) · 2008
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsMcGill University
FundersMcGill UniversityUniversité Laval
KeywordsInterpersonal communicationInterpersonal relationshipWorld Wide WebInformal learningCommunity of practicePsychologyInternet privacySociologyComputer scienceSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

This thesis focuses on investigating learning occurring in a web-based community of foodservice professionals, which is informally structured and based on voluntary participation, using concepts from the "communities of practice" (CoP) (Wenger, 1998) paradigm. As an informal learning environment, the web-based community was investigated based on three planes (Rogoff, 1998): Community (i.e., how does learning occur in the web-based community?), Interpersonal (i.e., how does learning occur between participants of the web-based community?), and Individual (i.e., how does individual learning occur through participating in the web-based community?). Under the umbrella of the online ethnographic approach, I applied mixed-method research combining multiple data sources (i.e., discussion transcripts, online survey, online interviews, ethnographic observation, and other documents) and analytical methods (i.e., descriptive framework for CoP, content analysis, transcript analysis, and descriptive statistics). In terms of the community plane, the web-based community was explored by applying the descriptive framework for communities of practice consisting of observable and measurable indicators in terms of organization, participation, and outcome. With regard to the interpersonal plane, I explored how learning occurs between participants of the WBC: How do participants interact with each other and what do they share through their interactions? To do that, the processes of interaction and learning were examined according to the size of threads (i.e., small, medium, and large sizes). For the individual plane, I examined (a) individuals' epistemological beliefs and (b) individuals' change of roles in relation to the degree of participation. Based on the results investigated in each plane, I discussed general characteristics of this web-based community as informal learning environment, effective features fostering interaction and learning in this web based community, and possible trajectories of the web-based community evolving for a community of practice. The importance of this study lies in its contributions to the conceptual framework (i.e., descriptive framework for communities of practice) and the methodological approach (i.e., multi-layered analytical approach) developed and applied in this thesis. The descriptive framework enables us to identify some defining features that distinguish communities of practice from other structures and hence to establish guidelines for monitoring how communities of practice evolve and what makes them evolve in successful ways. In addition, this study offers useful implications for designing and supporting web-based communities even in formal and non-formal learning environments. Because this study employed an exploratory, interpretive approach and concentrated on the breadth of learning in a web-based community through different planes, the results offer broader aspects of learning rather than specific, intensive issues of learning in this web-based community. Therefore, further studies are suggested along with the issues derived from this thesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.104
GPT teacher head0.366
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2008
Admission routes2
Has abstractyes

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