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Record W2296298829 · doi:10.1039/c5rp00194c

The efficacy of interactive analogical models in the instruction of bond energy curves in undergraduate chemistry

2016· article· en· W2296298829 on OpenAlexafffund
Vijay Shahani, Jodie Jenkinson

Bibliographic record

VenueChemistry Education Research and Practice · 2016
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAnalogyWitnessAnalogical reasoningComputer scienceEnergy (signal processing)Test (biology)Mathematics educationPsychologyMathematicsProgramming languageEpistemology

Abstract

fetched live from OpenAlex

We explored analogies used for introducing students to the concept of potential energy wells. Two analogy systems were developed, a spring system and a novel system consisting of electrostatic spheres. These two, distinct analogies were housed within an interactive tool that allowed students to manipulate the analogous systems and witness changes to potential energy curves in real time. A pre-test/post-test evaluation provided insight into the impact the formulation of an analogy system can have on understanding. Students modified written descriptions to include new details in accordance to the structure-mapping theory of analogies. However, students failed to correct visual descriptions of energy wells. The failure of participants to apply key concepts after using the interactive and animated analogy systems highlights the importance of designing for education.

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)0.009
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.083
GPT teacher head0.463
Teacher spread0.379 · 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 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

Citations20
Published2016
Admission routes2
Has abstractyes

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