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Record W2969685106

Chemistry simulations and embodied cognition: Exploring design, model generation, and collaboration

2005· article· en· W2969685106 on OpenAlexaff
Phillip Jeffrey, Samia Khan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmbodied cognitionCognitionComputer scienceChemistryCognitive scienceHuman–computer interactionArtificial intelligenceNeurosciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

The GEM cycle is a teaching approach with the objective of fostering understanding of unobservable phenomena in science, such as electricity, cellular respiration, or molecular structures. Simulations have enabled science students to experience and support the development of mental models. Traditionally, computer simulations have been used to support individual learning and inquiry, the cognitive advantages of paired and small group collaboration is becoming more apparent. Embodied cognition is a theoretical approach of relevance to learning contexts which defines cognition as embodied within one’s interaction with their environment. We have identified several objectives for enriched understanding in the research field of science education and student dyad collaboration. Our overall goal is to further understand how students develop conceptual understanding in the context of these interactions using a novel chemistry simulation designed by our research team. We have developed a number of research questions that study student collaboration and the possible emergence of shared understanding and mental model construction. Our preliminary research is designed to empirically evaluate our questions and facilitate the design of educational simulations for collaboration. Author Keywords Simulations, embodied cognition, Discourse, common

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.234
GPT teacher head0.401
Teacher spread0.168 · 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 designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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