ASSESSING THE IMPACT OF INTEGRATING INDIGENOUS KNOWLEDGES AND PERSPECTIVES IN ENGINEERING CURRICULA ON STUDENTS’ LEARNING: THE DEVELOPMENT OF A CLASSROOM CASE STUDY
Bibliographic record
Abstract
Indigenous Peoples in Canada have practiced sustainability for centuries. Their knowledges, perspectives and design principles are applicable on both a local and global scale especially in our quest to find sustainable approaches to food security, energy independence, and climate change impacts. However, the opportunities for Indigenous Peoples to fully participate and formally offer knowledge and guidance on sustainable development in engineering education have been limited. Engineering training in Canada requires students to develop competency in the area of assessing the impact of engineering on society and the environment. Within this competency is the ability to understand and apply the concepts of sustainability to engineering activities. Engaging with Indigenous Peoples to understand their perspectives on engineering and society provides a platform to critically assess existing engineering curricula, expand the concept of sustainability, and come closer to a common place of understanding. Understanding the impact of incorporating Indigenous perspectives in the curricula on students’ learning and understandings will help inform the further incorporation of Indigenous perspectives in engineering education. This paper presents the research methodology and instruments for a case study designed to explore students’ learning in one engineering course that integrates an Indigenous Elder’s perspectives on how to effectively communicate, engage, and obtain local knowledge on engineering projects with Indigenous communities in Manitoba. Findings will be used to inform engineering curriculum design that are enhanced by Indigenous knowledges and perspectives.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".