Building Pipelines into High-Impact Learning Experiences
Bibliographic record
Abstract
As colleges and universities increasingly emphasize the importance of High-Impact Learning Experiences (HILEs) in fostering deep learning, equity, and student success, a critical challenge persists: many students remain unaware of these transformative opportunities until later in their academic journey, if at all. To address this gap in early exposure, two complementary pedagogical approaches were piloted with the aim of cultivating interest and engagement with HILEs from the outset of students’ college experiences. The first, termed the Spark Engagement (SE) approach, involves integrating concise, student-centered modules into existing courses. These modules are intentionally designed to foreground HILEs and prompt students to reflect on their academic and personal goals through interactive, course-relevant activities. The second approach, Enlighten, Engage, Emerge (E3), provides students with curated, discipline-specific examples of HILEs that directly connect with the material being taught in class, thereby making abstract opportunities tangible and relevant. This article examines the conceptual foundations, implementation strategies, and preliminary outcomes of both SE and E3. Drawing on qualitative and quantitative data from pilot implementations, it highlights effective practices, common challenges, and lessons learned. It also outlines plans for scaling and refining these approaches within and beyond the pilot institutions. Ultimately, this work aims to inform and inspire educators, administrators, and institutional leaders seeking proactive and scalable strategies to introduce students, particularly those from historically underserved populations, to the full range of high-impact educational opportunities available to them.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".