Reframing the narrative on summer school: How student partnership led to meaningful change
Bibliographic record
Abstract
At our institution-Eastern Kentucky University (EKU), a regional, teaching-focused university in the United States-summer session (known as EKU Summer) now represents an important academic opportunity for students.As Kopp (2016) has explained, summer session programs are a "problem-solving and innovating force within the university to help deal with . . .issues, such as reducing time to earn a degree, improving retention rates, creating capacity, and supporting enrollment management" (p.3).In the redesign of EKU Summer, a large-scale initiative involving numerous university stakeholders, the Students-as-Partners (SaP) model, and, in particular, student partners were central to the process.Through reflection based on the lived experience of partnership, we offer ways in which this model was critical to the success of the EKU Summer redesign.The process involved implementing a number of important changes to EKU Summer, including offering students more online course options, scheduling courses at times that are preferable to students, and prioritizing terms that are ideal for the course and delivery.By using this approach, which was driven by partnership and student involvement, fewer classes overlapped, so there was less competition for students' time, and students were more available to take multiple courses if they chose to do so.Making these large-scale changes, however, meant involving students and student input in each phase throughout the process.We used the partnership model, ensuring a broader scope to include student voices.This change made the process richer and more directly linked to the members involved through ongoing collaborations wherein each partner's perspective on the project added value and factored in the EKU Summer redesign.In this reflective essay, an undergraduate nursing student, a graduate creative writing student, and a faculty member (the assistant provost of a multi-unit program, summer coordinator, and professor of English) reflect on the process of situating student partners in reenvisioning EKU Summer.In this model, students played a leading role in shaping the academic planning, research, design, and implementation of all areas of EKU Summer.This included significant decisions such as courses scheduled, standard terms and times when the majority of courses would be offered to best suit students, and delivery method (on-ground or online).
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.046 | 0.039 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.006 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".