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Record W2794793058

A string number-line lesson sequence to promote students' relative thinking and understanding of scale, key elements of proportional reasoning

2018· article· en· W2794793058 on OpenAlexaboutno aff
Annette Hilton, Geoff Hilton

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
FundersAmerican Educational Research Association
KeywordsProportional reasoningMathematics educationNumber lineProportionality (law)Absolute (philosophy)Scale (ratio)Critical thinkingMathematicsEpistemologyPhysicsLawPhilosophyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Proportional reasoning involves relative thinking: the ability to think about multiple quantities simultaneously, and in relative terms as opposed to absolute terms (Ontario Ministry of Education, 2012). Concepts related to relative and absolute thinking and the ability to think relatively and multiplicatively are essential to understanding proportionality (Hilton, Hilton, Dole, & Goos, 2015). It takes time for students to develop proportional reasoning and without targeted teaching, many students fail to develop the important skills and conceptual understanding that underpins it (Bangert- Drowns, Hurley, & Wilkinson, 2004; Kastberg, D'Ambrosio, & Lynch-Davis, 2012; Lamon, 2012). Proportional reasoning is essential for students to succeed in many mathematical areas, including ratio and proportion, measurement and unit conversions, geometry, and probability. It is also necessary in other subjects, such as geography and science (Akatugba & Wallace, 2009). In fact, scale and proportion have been identified as crosscutting concepts fundamental to understanding and reasoning in science (National Research Council, 2012).

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.053
GPT teacher head0.352
Teacher spread0.299 · 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 designTheoretical or conceptual
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

Citations2
Published2018
Admission routes1
Has abstractyes

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