The Design And Impact of a New Biomedical Seminar Deconstruction Course on Undergraduate Learning‐A Pilot Study
Bibliographic record
Abstract
We designed and piloted a research seminar deconstruction course to expose second year undergraduate students to high impact research, bridge the gap in their understanding of the scientific process and provide the opportunity to formulate their own research proposals. Deconstruction sessions included small and large discussion groups using flipped model of classroom teaching facilitated by problem sets with strong emphasis on web‐based resource library developed specifically for each seminar. Students were assessed based on written mini‐journals, seminar summaries, problem sets, and the oral presentation of their research proposal. Didactic sessions on guidelines for scientific presentation and writing research proposal were built‐in the course. Semi‐structured interviews and pre and post course surveys administered to ten students registered in the inaugural course show key learning gains in written and oral presentation of scientific material, interpretation of scientific results, understanding of the scientific process, confidence in developing realistic scientific research questions. The key challenges reported by students included coping with the high workload and planning their research proposal. The majority of the students reported learning skills that were transferable to other classes highlighting time management and critical thinking skills which they felt would enable them succeed in upper level research streams; some even described it as their favorite course. Support or Funding Information UBC Centre for Teaching, Learning & Technology
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".