Effects of COVID-19 on First-Year Undergraduate Research in Physical Geography
Bibliographic record
Abstract
Having confirmed that including research in first-year undergraduate teaching can actually help students understand the research process, link research with concepts, and improve both their academic and professional skills, we intended to evaluate how this experiential learning component fared during the COVID-19 challenge. For a first-year three-credit physical geography class, we have included a First Year Research Experience (FYRE) project for six iterations. A cluster analysis grouped students’ perceptions obtained from survey questions into five categories, from high to low. The results showed an overall improvement in perception of the FYRE during the pandemic, driven primarily by soft-skill development related to time management and self-motivation. Students were also able to better connect the research project with the theoretical content of the course. Components of the FYRE that suffered during the pandemic include engaging with course instructors and completing the oral presentation phase of the research. Soft-skill development continued through the second year of the pandemic, although students’ dissatisfaction with continued restrictions on in-person contact was evident.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".