A discipline-specific R manual improves students' skills and confidence in their chosen field
Bibliographic record
Abstract
Students enter the university classroom with varying levels of quantitative skills. This includes varying numerical proficiency and varying levels of proficiency with coding languages. As data science becomes more prevalent in scientific research, and use of statistical programming software is increasingly common, there have been growing calls to increase exposure to programming skills in undergraduate-level courses. ‘R’ is currently the most popular statistical programming software across ecology and evolutionary biology. The initial steep learning curve of R and the limited availability of resources for beginners result in an incompatibility between resources and students’ needs. To address this gap, we created a student-facing and department-specific R manual for use as a learning and teaching resource. Through quantitative surveys in a large-enrollment second year ecology course, we assess the effectiveness of the manual and R-based lab activities in improving student R skills and confidence. We also conducted a survey of graduate student teaching assistants and faculty who indicated that the manual meets the current learning objectives of the department. Our results highlight the variation in confidence and skills among second-year students and show that lab training and the R manual helped to close learning and skills gaps for students lacking previous experience. These results emphasize the importance of early exposure to statistical programming opportunities and activities early in undergraduate science courses to help increase skills and confidence among students. This research was approved by the University of Toronto’s Social Sciences, Humanities and Education Research Ethics Board.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".