A Two-Eyed Seeing Teaching and Learning Framework for Science Education
Bibliographic record
Abstract
Abstract Worldwide, education jurisdictions are looking for authentic ways to address First Nations perspectives in the K-12 curriculum, including science education. At the same time, there have been ongoing efforts to integrate authentic and engaging approaches to teaching science, including those that are student-centred, inquiry-based, multimodal, and linked to meaningful contexts. Both goals may be realised through the principle of Two-Eyed Seeing (TES), which seeks to integrate the strengths of Indigenous ways of knowing with one eye, and Western ways of knowing with the other eye, for the benefit of all students. This theoretical paper presents a Two-Eyed Seeing for Science Education (TESSE) Framework, which brings together two pedagogical models. One is from a contemporary science perspective, the 5Es representation-rich inquiry approach, which scaffolds authentic student-centred conceptually focused learning experiences. The other is from an Indigenous perspective, the 8 Aboriginal Ways of Learning, which illustrates different ways of knowing—many of which are familiar with First Peoples across the world (e.g., place-based, visual, holistic). The TESSE Framework aims to act as a strengths-based interface between the two knowledge systems to support a culturally responsive approach to teaching and learning science. It is designed to support meaningful connections through curriculum and pedagogy in ways that are contextually relevant to place. Through empirical investigation and in collaboration with local communities, the Framework has the potential to inform current approaches to science education in schools and universities and provide a pathway towards decolonisation and reconciliation.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".