Research assistants’ experiences of co-creating partnership learning communities for learning and teaching in higher education
Bibliographic record
Abstract
Calls for enhancing student engagement in higher education have offered strong arguments for student-faculty partnerships in teaching and learning. Drawing on a conceptual model of partnership learning communities (PLC), we investigate the experiences of two undergraduate research assistants (co-authors of this paper) who participated in a PLC within a Scholarship of Teaching and Learning research study. In this paper, we use data from transcripts of four research conversations occurring over a three-year period. Evidence of research assistants’ experiences was co-analyzed using benefits and challenges identified in the literature. Our findings reveal that our PLC helped these research assistants develop student agency and provided opportunities for reflection on learning. We conclude that participating in our PLC helped the two research assistants develop deeper pedagogical relationships amongst themselves and with the faculty partners. Moreover, our study directly contributed to the development of our bachelor of education degree program while ensuring students were partners in that process.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".