Bibliographic record
Abstract
Introduction: R language is a powerful tool used in a wide array of research disciplines and owes a large amount of its success to its open source and adaptable nature. The popularity of R has grown rapidly over the past two decades and the number of users and packages is increasing at a near exponential rate. This rapid growth has prompted a number of formal and informal online and text resources, the volume of which is beginning to present challenges to novices learning R. Students are often first exposed to R in upper division undergraduate classes or during their graduate studies. The way R is presented likely has consequences for the fundamental understanding of the program and language itself; user comprehension of R may be better if learning the language itself followed by conducting analyses, compared to someone who is learning another subject (e.g. statistics) using R for the first time. Consequently, an understanding of the approaches to R education is critical. Methods: To establish how students are exposed to R, we used a survey to evaluate the current use in Canadian university courses, including the context in which R is presented and the types of uses of R in the classroom. Additionally, we looked at the reasons professors either do or don’t use/teach R. Results: We found that R is used in a broad range of course disciplines beyond statistics (e.g. ecology) and just over one half of Canadian universities have at least one course that uses R. Discussion and Conclusions: Developing programming-literate students is of utmost importance and our hope is that this benchmark study will influence how post-secondary educators, as well as other programmers, approach R, specifically when developing educational and supplemental content in online, text, and package-specific formats aiding in student’s comprehension of the R language.
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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.187 | 0.378 |
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".