Developing a university learning, teaching and research framework through practice conversations
Bibliographic record
Abstract
Purpose This project engaged faculty, students, alumni and staff in re-visioning their university's learning, teaching and research framework. An extensive consultation process allowed participants to explore, discuss and critically reflect on effective practice. Design/methodology/approach This action research project provided a process for university community members to engage in practice conversations. In phase 1, focus groups and campus community discussions elicited the diverse perspectives of the community. The design-thinking process of discovery, ideation and prototyping aligned with the action research cycles to help a working group create a learning and teaching framework prototype based on the findings. In the second phase, surveys were used to elicit community members' responses to the prototype, which was then refined. Findings The prototype was organized into three overarching categories, each containing several attributes. The attributes of the “Applied and Authentic” category were: interdisciplinary and transdisciplinary; experiential and participatory; flexible and individualized; outcomes based; and openly practiced. The attributes of the “Caring and Community-Based” category were: inclusive and diverse; community-based; supportive; team-based; co-creative; and place and virtual space-based. The attributes of the “Transformational” category were socially innovative; respectful of Indigenous peoples and traditions; impactful; and reflective. Originality/value This article should interest higher education institutions seeking to engage faculty, staff, students and others in practice conversations to develop a learning, teaching and research strategy. This research demonstrated that fostering practice conversations among diverse community members can be a powerful process for creating a common and integrated vision of excellent learning, teaching and research practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| 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".