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Record W4214872247 · doi:10.26434/chemrxiv-2022-kb1s1

Investigating educators’ perspectives towards systems thinking in chemistry education from international contexts

2022· preprint· en· W4214872247 on OpenAlexafffund
Alisha Szozda, Kathryn Bruyere, Hayley Lee, Peter G. Mahaffy, Alison B. Flynn

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsThe King's UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChemistryChemistry educationThematic analysisPsychologyMathematics educationEngineering ethicsPedagogyEngineeringSociologyQualitative researchSocial psychology

Abstract

fetched live from OpenAlex

Systems thinking in chemistry education (STICE) has been proposed as an approach that could better equip students with abilities to connect their chemistry knowledge with other disciplines, with the skills needed to tackle complex global issues. However, educational change in chemistry is a complex effort that involves many interconnected factors that enable or hinder chemistry educators’ adoption of new pedagogical approaches. Using an adapted version of the Teacher-Centered Systemic Reform (TCSR) model, we investigated factors that connect with chemistry educators’ willingness and ability to implement a STICE approach in their courses. We surveyed a group of 56 secondary and post-secondary chemistry educators from ten different countries, to capture chemistry educators’ perspectives towards a STICE approach. Through thematic analysis of responses, we found that educators’ willingness and ability to implement STICE is influenced by their knowledge, beliefs, experiences, contextual and personal factors. We discuss specific aspects of the reform model that experts and administrators can address to reduce barriers to implement and engage with STICE. We also highlight future chemistry education research that is needed to explore specific aspects of educators’ perspectives and STICE more broadly.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 designBench or experimental
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 routes2
Has abstractyes

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