Connect & flourish – Indigenous learning circles in life sciences and physical sciences
Bibliographic record
Abstract
During undergraduate studies, fostering a sense of community is essential for both academic success and personal development. Intentional inclusion of Indigenous ways of knowing into course materials and activities plays a key role in the process of reconciliation (Barkaskas & Gladwin, 2021; Battiste, 2010) and contributes to the decolonization and Indigenization of the curriculum (Barkaskas & Gladwin, 2021; Hanson & Danyluk, 2022). One example is the incorporation of Indigenous talking Circles, which are supportive and safe environments (Brown & Di Lallo, 2020). Relationships may be cultivated and connections with other Circle members are established (Brown & Di Lallo, 2020). Students in three undergraduate courses at the University of Waterloo engaged in Learning Circles. In a third-year biology course, Learning Circles were implemented to help clarify any difficult concepts after students reflected on their learning. In a first-year physics course, Learning Circles were implemented in tutorials where students shared their problem-solving approach to difficult physics problems. In a second-year physics course, modified Learning Circles were incorporated where students were able to solve physics problems in both in-person and online environments. A survey was created to gather insightful feedback from students on how Learning Circles enhanced their overall learning experience and established a community amongst learners. In general, students found that Learning Circles was a preferred form of groupwork and many strongly agreed that the Learning Circles helped form a sense of community. This study has been reviewed and received ethics clearance through the University of Waterloo Research Ethics Board. References Barkaskas, P., & Gladwin, D. (2021). Pedagogical Talking Circles: Decolonizing Education Through Relational Indigenous Frameworks. Journal of Teaching and Learning, 15, 20-38. Battiste, M. (2010). Nourishing the learning spirit. Education Canada, 50(1), 14-18. Brown, M., & Di Lallo, S. (2020). Talking Circles: A Culturally Responsive Evaluation Practice. American Journal of Evaluation, 41, 367-383. Hanson, A., & Danyluk, P. (2022). Talking Circles as Indigenous Pedagogy in Online Learning. Teaching and Teacher Education, 115, 103715.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".