The Advanced Interdisciplinary Research Laboratory: A Student Team Approach to the Fourth-Year Research Thesis Project Experience
Bibliographic record
Abstract
The advanced interdisciplinary research laboratory (AIRLab) represents a novel, effective, and motivational course designed from the interdisciplinary research interests of chemistry, physics, biology, and education development faculty members as an alternative to the independent thesis project experience. Student teams are assembled to work toward the completion of an interdisciplinary research project. Each team is composed of at least one student from a differing area of specialization (e.g., biology, biotechnology, chemistry, and physics), and projects are based on current trends in research. The inaugural project was the development of a portable DNA sequencer for in-field species identification; a project at the interface between physical and life sciences and that holds the potential for real-world application and positive social change. This work describes the details underlying the design and implementation of the AIRLab course, and includes an account of the method of student team assembly, the selection of a suitable research challenge, the specialized training provided in Agile project management, the methods used to achieve cohesive team dynamics, the learning outcomes from this experience, future directions that will be pursued for course improvement, and how our undergraduate degree-level expectations were met at an advanced level.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".