Three models for embracing student expertise in the development of pedagogical partnership programs
Bibliographic record
Abstract
Nearly a decade ago, two colleagues and I defined student-faculty (student-staff) pedagogical partnership as premised on the notion that students have expertise to contribute to processes of preparing for, reflecting on, and revising teaching and learning practices (Cook-Sather et al., 2014).This expertise is based in part on students' experiences as students.It is also informed by unique intersections of socio-cultural identities and lived experiences that students bring to educational contexts and analyses (Brown et al., 2020;Cook-Sather et al., 2021;Doktor et al., 2019;Matthews, 2017) and by students' engagement in sense-making about their identities and life experiences.As pedagogical partnership work expands around the globe, I am seeing undergraduate students at my own and other institutions develop another form of expertise: in pedagogical partnership work itself.I embarked on developing student-faculty pedagogical partnership work in 2006 at Bryn Mawr and Haverford Colleges, two liberal arts institutions in the mid-Atlantic region of the United States.With student, faculty, and staff collaborators and several generous grants from The Andrew W. Mellon Foundation, I co-created Students as Learners and Teachers (SaLT), the signature program of the Teaching and Learning Institute (TLI) in this bi-college consortium.Through SaLT, undergraduates take up semester-long, paid positions as pedagogical partners to faculty members.As pedagogical partners, these students are neither enrolled in nor necessarily have knowledge of the subject matter of the course on which they and their faculty partners focus.They conduct weekly classroom observations, meet weekly with their faculty partners, and meet weekly with me and other student partners to discuss how best to be in dialogue and collaboration with faculty partners.Hundreds of faculty and students have participated in the SaLT program, and options for participation have expanded beyond this basic model (for a list of other programs, visit the TLI's Programs and Opportunities page).Because of my decades of experience directing SaLT, I have been invited to support a wide range of colleges and universities around the world in developing partnership programs (see partial lists at TLI's Pedagogical Partnerships at Other Institutions page and Developing Pedagogical Partnerships).Since staffing budgets are always limited, I have begun in this consulting work to recommend hiring students-while they are undergraduates or soon after they graduate-into limited-term roles to conceptualize, facilitate, co-create, or otherwise support the development of pedagogical partnership programs.This approach not only acknowledges student expertise, it also builds institutional structures that affirm that
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".