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Record W2223059457 · doi:10.1108/jpcc-09-2015-0007

Essential features of effective networks in education

2016· article· en· W2223059457 on OpenAlexaff
Santiago Rincón-Gallardo, Michael Fullan

Bibliographic record

VenueJournal of Professional Capital and Community · 2016
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsCLARITYGeneral partnershipAccountabilityComputer sciencePublic relationsKnowledge managementPolitical sciencePsychologyProcess managementMathematics educationBusiness

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to advance clarity and precision around effective action in networks, understood as collaboration that: first, deepens the learning and engagement of students and adults; second, enhances the professional capital of teachers and leaders; and third, becomes a positive force of whole system improvement. It distills eight essential features of effective networks by fleshing out key lessons from existing research and from emerging education network developments in the English-speaking world and Latin America. It then discusses three shifts required for a new partnership between networks and central leadership to turn networks into forces of educational system renewal. Design/methodology/approach – Two sources of evidence were identified and reviewed: first, literature reviews and studies aimed at identifying characteristics of effective networks in education; and second, network case studies and R & D initiatives that used networks as their improvement strategy and had demonstrated positive impact on student outcomes or on one or more professional capital variables often associated with improved student outcomes. To distill the eight essential features of effective networks and three required shifts in the relationship between networks and central leadership, the authors engaged in an iterative process of thematic analysis (Boyatzis, 1998) deliberately searching for key characteristics and processes describing effective collaboration. The list was revised for completeness and parsimony. Findings – The eight essential features of effective networks identified are: first, focussing on ambitious student learning outcomes linked to effective pedagogy; second, developing strong relationships of trust and internal accountability; third, continuously improving practice and systems through cycles of collaborative inquiry; fourth, using deliberate leadership and skilled facilitation within flat power structures; fifth, frequently interacting and learning inwards; sixth, connecting outwards to learn from others; seventh, forming new partnership among students, teachers, families, and communities; and eighth, securing adequate resources to sustain the work. The three required shifts in the relationship between networks and central leadership are: first, from supply driven to demand driven; second, from compliance oriented to learning oriented; and third, from bureaucracy to movement. Research limitations/implications – The key limitation derives from the scarce available evidence to date causally – or even co-relationally – connecting network activities with improved student learning. This paper summarizes what is known to date about effective collaboration in networks and advance a theory of action that causally links network activities with improved student outcomes and enhanced professional capital. This theory of action, summarized in eight essential features, simultaneously offers key hypotheses for social network theory in education and actionable guidelines to develop effective networks. Practical implications – The eight essential features of effective networks and the three required shifts in the relationship between networks and central leadership presented here were intentionally framed as action oriented. They offer a clear and actionable set of guidelines to develop effective networks. Social implications – The power of networks as vehicles to dramatically improve schools and entire educational systems is yet to be realized. This paper offers guidelines to enhance the effectiveness of networks, and thus contributes to the realization of the yet unfulfilled promise of networks. Originality/value – This work adds originality and value in three ways: first, it draws from both existing studies on effective networks and successful and promising education networks; second, its findings apply to multiple configurations of networks, across multiple contexts – existing publications place their focus on specific network configurations or a specific network case or initiative; third, it looks at effective collaboration in networks from the dual perspective of local problem solving and whole system improvement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0040.017
Scholarly communication0.0080.016
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.383
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations197
Published2016
Admission routes1
Has abstractyes

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