Exploring and Enhancing Experiential Teaching and Learning in Social Justice-Oriented Education
Bibliographic record
Abstract
Experiential education is a unique approach to teaching which can enhance student learning, engagement with material, and understanding of real-world situations. It particularly has potential to strengthen education in the social sciences, such as courses focused on topics related to social justice. This SaPP project aimed to explore experiential education in more detail in the literature and to map existing models of experiential education in disciplines related to social justice to identify ways to integrate experiential education more into SOCI 2170: Foundations in Social Justice, an undergraduate sociology course at Carleton University. The literature review conducted revealed service learning, problem-based learning, land-based learning, and place-based education as four key types of experiential education. The review of public course syllabi uncovered many samples of experiential education used in similar courses at other institutions. These literature findings and applicable real-world examples provide a strong foundation to improve experiential learning in SOCI 2170 for future years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".