Investigating educators’ perspectives towards systems thinking in chemistry education from international contexts
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".