The Development and Delivery of a Multidisciplinary Research Course for First-Year International Science Students
Bibliographic record
Abstract
Students who engage in undergraduate research experiences acquire many benefits, including an understanding of how scientific knowledge is constructed, recognition that knowledge can be complex and uncertain, and the habit of viewing knowledge critically. This paper describes a first-year two-course sequence that provides multidisciplinary opportunities for international science students to engage in the research process and present at a student-led research conference. We describe course goals and structure, and discuss whether the goals were attained using instructor reflections, student performance, and student survey data. We also evaluate the impact of changes to the curriculum between Year 1 and Year 2. In both years, we found that students engaged meaningfully with the research process and began to understand how scientific knowledge is created. We also found that a modular model with front-end support worked better for instructors as compared to a continuous individualized project mentorship model. This modular approach involved structured pre- and post-class assignments within discipline-specific themes containing examples of the research process embedded into the discipline. These discipline-specific modules were followed by modules covering broader research process themes. We encourage instructors who are thinking of delivering a similar research-based course for first-year students to provide support via example research questions and other example templates for student submissions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".