Potential for Chemistry in Multidisciplinary, Interdisciplinary, and Transdisciplinary Teaching Activities in Higher Education
Bibliographic record
Abstract
For some professionally, vocationally, or technically oriented careers, curricula delivered in higher education establishments may focus on teaching material related to a single discipline. By contrast, multidisciplinary, interdisciplinary, and transdisciplinary teaching (MITT) results in improved affective and cognitive learning and critical thinking, offering learners/students the opportunity to obtain a broad general knowledge base. Chemistry is a discipline that sits at the interface of science, technology, engineering, mathematics, and medicine (STEMM) subjects (and those aligned with or informed by STEMM subjects). This article discusses the significant potential of inclusion of chemistry in MITT activities in higher education and the real-world importance in personal, organizational, national, and global contexts. It outlines the development and implementation challenges attributed to legacy higher education infrastructures (that call for creative visionary leadership with strong and supportive management and administrative functions), and curriculum design that ensures inclusivity and collaboration and is pitched and balanced appropriately. It concludes with future possibilities, notably highlighting that chemistry, as a discipline, underpins industries that have multibillion dollar turnovers and employ millions of people across the world.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".