Can Open Pedagogy Encourage Care? Student Perspectives
Bibliographic record
Abstract
As a response to the increasing commercialization of postsecondary education, educators argue for a practice of care in education. Open pedagogy (OP) seems like an ideal practice where care, trust, and inclusion can be realized. OP is characterized as a democratic and collaborative pedagogical practice, in which students and teachers work to co-create learning and knowledge using openly licensed materials, open platforms, and other open processes. The purposes of this study were, first, to reveal ways students in postsecondary institutions perceive care and, second, to determine how students suggest OP can be used to create an open/caring learning process. A task-oriented focus group method engaged students from four teaching-focused institutions. The students created open cases on social issues for class discussion and reflected on care and OP processes in postsecondary settings. Using four elements of the ethics of care—attentiveness, responsibility, competence, and trustworthiness—as conceptual categories, the study examined students’ experience of care and care in OP using affective coding and thematic analysis. The results showed that through OP, with teacher support and explicitly designed practices of care, students can assert their agency, have quintessential roles in creating and participating in highly relevant curriculum and importantly, care about others, and be cared for. OP is a process able to involve a diverse population of students and embody care as an all-encompassing practice.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".