The Scholarship of Teaching and Learning in an Age of Accountability: Building Bridges
Bibliographic record
Abstract
In recent years, as pressures for accountability have increased in higher education, some members of the Scholarship of Teaching and Learning (SoTL) community may worry that the inquiry-based, improvement-focused practices they advocate could be put at risk by easy-to-administer, one-size-fits-all forms of assessment, quality assurance, and administrative control. But while acknowledging both real and perceived tensions between these two movements, we also examine some of the ways and settings in which they are converging, featuring a number of international examples in which external quality and assessment mandates have been employed to support SoTL-like work. We look, too, at the roles that scholars of teaching and learning can play as mediators and brokers between the two movements, helping to translate accountability requirements into opportunities for improvement. In short we argue that these two movements present opportunities for each other. SoTL can contribute to what is, or should be, the central goal of accountability: ensuring and improving the quality of student learning. The accountability movement, for its part, can provide a new context for integrating and valuing SoTL as a force for positive change on campuses and beyond. Taken together, the two approaches can make meaningful contributions to higher learning today. The paper concludes with recommendations to the SoTL community for building bridges between the two movements.
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.087 |
| Scholarly communication | 0.030 | 0.047 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".