Living Large
Bibliographic record
Abstract
This chapter describes the journey of a multidisciplinary faculty learning community (FLC) focused on student engagement in large classes and the scholarship of teaching and learning (SoTL). The chapter shows how interest in the FLC was initially generated and how that interest turned into a sustained multi-year research project. It shares the journey and the lessons learned, from the initial meeting—where potential participants were introduced to concepts of FLCs—to the current state of the research project. The chapter further discusses how the topic of interest was chosen, presents some of the preliminary surveys, discussions, and resources that guided that decision, and explains the process that led to the proposed research framework—with an emphasis on providing authentic opportunities for all members to participate. The importance of a SoTL approach to the FLC’s research and how that approach facilitated knowledge transfer activities is also highlighted, as it relates to the FLC’s primary focus of enhancing student learning within large classes. This focus on student learning helped guide the research undertaken on student engagement and our participation in teaching observations of the classes being studied. The FLC obtained grant funding to support an undergraduate student researcher, who played a fundamental role in assisting the FLC with reviewing the literature, identifying a research topic, and importantly, offered their own authentic experience throughout the FLC’s journey.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.292 | 0.108 |
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".