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Record W4293529805 · doi:10.1139/cjc-2021-0162

Conceptual change in chemistry schematic modelling: a cross-sectional study of 10th–12th-grade Belgian students

2022· article· en· W4293529805 on OpenAlexvenueno aff
Hamad Karous, Brigitte Nihant, Bernard Leyh

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsSchematicMathematics educationPsychologyConceptual changeChemistryRelevance (law)PerceptionAdaptation (eye)Quality (philosophy)EngineeringEpistemology

Abstract

fetched live from OpenAlex

Practice-oriented studies on conceptual change in chemistry education remain relatively scarce. In agreement with the constructivist approach, in which learners build their own cognitive structure, many results have shown that learning through modelling positively contributes to conceptual adaptation. From this point of view, this study presents a cross-sectional study of secondary school learners’ schematic modelling abilities in the upper secondary school. The schematic modelling skills and competences of 216 students (15–18 years old, 10th–12th grade), ranging from purely macroscopic conceptions to an adequate interplay between the three levels of Johnstone’s triangle, have been assessed within selected contextualized situations. The data were collected using a three-part instrument, namely the analysis of the information contained in a graphical scheme, the perception of the role and relevance of schematic modelling, and the autonomous production of schematic modelling. The collected data show that 12th-grade students have a significantly higher capability of analysing the information of a scheme compared to 10th and 11th grades. Moreover, autonomous schematic modelling skills follow the same trend. The data also provide some evidence that students with better model analysis competences design higher quality autonomous models at the submicroscopic level.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.162
GPT teacher head0.411
Teacher spread0.249 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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