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Record W2130373961 · doi:10.1002/sdr.366

Thinking about systems: student and teacher conceptions of natural and social systems

2007· article· en· W2130373961 on OpenAlexaboutno aff
L. Booth Sweeney, John D. Sterman

Bibliographic record

VenueSystem Dynamics Review · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationQuarter (Canadian coin)Causal loop diagramNatural (archaeology)Systems thinkingPsychologySystem dynamicsPedagogyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Many in the system dynamics community argue that children are natural systems thinkers. Here we study how middle school students and teachers think about everyday settings involving feedback, stocks and flows, time delays and nonlinearities, prior to any formal training in these concepts. We develop instruments to elicit understanding of systems concepts and test them with students and teachers from two middle schools in the U.S.A. We find, with some exceptions, generally limited intuitive systems thinking abilities. “Open‐loop” or one‐way causal thinking is common. Explanations lack references to time horizons and time delays. Significant misconceptions of stock and flow structures appear regardless of age. Teachers generally outperformed students, although one‐quarter of the students performed at the median level for teachers. We discuss the nature of students' and teachers' intuitive models of dynamic systems, explore potential barriers to understanding dynamic systems, and discuss implications for effective teaching of systems concepts. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.423
Teacher spread0.342 · 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 designQualitative
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

Citations258
Published2007
Admission routes1
Has abstractyes

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