Coupling discovery-based learning and apprenticeship research experiences: a novel undergraduate laboratory course model
Bibliographic record
Abstract
Apprenticeship research experiences (AREs) provide undergraduate students with real-world opportunities to engage in authentic experiment-based research as integral members of the supervisor's laboratory team. While AREs have been proposed to support students' confidence and competencies in the laboratory, they can also present practical barriers for effective pedagogical and fair implementation in undergraduate programs. For example, as AREs are conducted in authentic research environments independent of a course context, they are often not equipped with the pedagogical structure and guided instruction to best support student learning. Moreover, students frequently compete to secure a limited number of ARE placements such as summer research positions, honors thesis students, or scholarship recipients. As a result, many students who aim to complete an ARE within their undergraduate degree may never receive the opportunity, raising questions of factors that may impact fair and impartial student eligibility for these research experiences. To address these barriers, our faculty developed an innovative undergraduate course that integrates a discovery-based training laboratory component and an ARE placement component directly within its structure. Here, we present the details of this unique course structure and provide practical resources and suggestions for implementation in similar laboratory courses in science-related undergraduate programs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".