Applying a Linked-Course Model to Foster Inquiry & Integration in Large First Year Courses
Bibliographic record
Abstract
Many first-year university courses are large and content-driven, which can contribute to low student engagement and difficulty involving students in the dynamic, cross-disciplinary nature of inquiry. Learning communities can address these goals, but their implementation often poses logistical challenges, especially in large courses. Here, we apply learning communities using a linked-course model to enhance student engagement and inquiry across three large, first-year biology courses. These three courses (Discovering Biodiversity, Molecular and Cell Biology, and Biological Concepts of Health) offer different contexts for biological inquiry, introduce key biological concepts, and are connected through jointly mapped learning outcomes, shared online skill workshops, and integrative learning communities composed of students from each course. Diverse modes of student learning are used across the three courses, and contemporary problems are explored within classes and small group seminars, which promote the development of skills necessary for inquiry. This course structure requires the coordinated scheduling of seminars and interdisciplinary projects but allows flexibility in the use of lecture periods and online content while offering increased resource efficiency. Collectively, these courses provide opportunities for integration, skill development, and problem solving. In contrast to many other forms of learning communities, this particular model promotes both a disciplinary foundation and crossdisciplinary applications for large numbers of students.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".