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Record W2395417611 · doi:10.29173/cmplct23022

A complexity approach to investigating the effectiveness of an intervention for lower grade teachers on teaching science

2016· article· en· W2395417611 on OpenAlexvenueno aff
Annemie Wetzels, Henderien Steenbeek, Paul van Geert

Bibliographic record

VenueComplicity An International Journal of Complexity and Education · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Context (archaeology)Scale (ratio)Management scienceComputer scienceEmpirical researchComplexity managementPoint (geometry)PsychologyEpistemologyMathematicsEngineering

Abstract

fetched live from OpenAlex

This article describes the effectiveness and sustainability of teacher professional development interventions from a complexity view point as well as a more ‘standard’ viewpoint. The first aim of this study is to give a theoretical overview of effective aspects of interventions regarding teachers’ professionalizing using recent literature. The second aim is to re-interpret effectiveness and effectiveness studies using a complexity approach. The third aim is to empirically illustrate a complexity approach to the effectiveness of interventions using a multiple case studyWe have described intervention specific aspects, teacher specific aspects, context specific aspects and implementation specific aspects and have shown in the cases that during an intervention these aspects intertwine and act as a complex dynamic system.The complexity approach to interventions has implications for future empirical research as well as for the design of interventions. In future research, results of small scale and large scale research should be combined, in order to obtain a better insight in all relevant aspects and their effect on teachers’ behavior. Research ought to concentrate on the effects that all contextual aspects have intertwiningly in order to design more effective interventions and with better implementation processes.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.345
GPT teacher head0.452
Teacher spread0.107 · 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.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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