Can first-year undergraduate geography students do individual research?
Bibliographic record
Abstract
Based on the affirmation in the scholarship of teaching and learning that adding research component into early geography classes is mutually beneficial to both instructors and students, this paper presents a case study that quantitatively articulates the effects of adding a research project into a first-year physical geography class on students’ academic performance. Pushing research into earlier stages of undergraduate students’ academics, even in large classes, can be very beneficial yet challenging because most students at this level have no experience in research; plus, they may still be adjusting to university life. Part of the Undergraduate Research Initiative called First Year Research Experience (FYRE) at the University of Saskatchewan in Canada invited faculty to embed a research component into first year undergraduate classes to align research and teaching. A two-year endeavour in a first year physical geography class resulted in some interesting outcomes. (1) The most challenging part of research for students was the research question formation. (2) Students valued the opportunity to conduct a research project. (3) Doing in-class research actually improved student performance as seen in the higher overall average grades. (4) Students who attained the highest exam marks were not those who attained the highest research project marks.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".