Exploring the Foundations for Student Success: A SoTL Journey
Bibliographic record
Abstract
This paper illustrates the chronology of a research project which began in 2010 and continues today. The research has evolved over time from a focus on the phenomenon (developing an understanding of student diversity and its impacts on student success), to experimental research (to learn the impact or benefits derived from the introduction of high impact practices), to a more complex understanding of the foundations for student success. The fourth stage of the research, which is just underway, divides our efforts into two distinct directions. The first is quantitative research utilizing institutional and learning management system data which was previously untracked and untapped. The second is a shift to employing more qualitative research tools aimed at advocacy and institutional change. Through each phase of the research the paper presents two distinct perspectives: First is the perspective of instructors-turned-SoTL-researchers as we muddle our way through understanding our challenges and learning how to use SoTL research methods to help guide the way. The second perspective is that of an established SoTL researcher, who provides commentary and guidance to our journey. Our hope is that the reader finds these two perspectives of a research journey both informative and valuable in providing insights into how a long-term research project might unfold.
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".