Student-Faculty Co-Creation of Open Educational Resources for Learning Applied Statistics with Open Source Software Tools
Bibliographic record
Abstract
In most university courses, students learn from textbooks they did not help develop. We present an innovative approach where undergraduate students collaborated with faculty to create an open-access, interactive web-based e-book for an introductory applied statistics course. This process transforms undergraduates into co-authors rather than passive readers, fosters an appreciation for reproducible research, encourages academic collaboration, and offers diverse, hands-on opportunities to acquire and apply new skills and technologies. Faculty also benefit from fresh ideas and new perspectives. This type of collaboration is important to explore, as evidence on student-faculty partnerships in developing course materials is sparse in the literature, particularly in a Canadian context and in the area of statistics. In this paper, we illustrate the contributions of undergraduate students to curricular innovation in the development of the e-book by describing the development process, methodology and software tools used, and reflect on our experience as collaborators alongside faculty. Our participation deepened our learning, while producing resources to benefit future learners. We highlight the potential of open-source technologies and student-faculty collaboration to support the development of adaptive, interactive, and accessible resources for statistical education.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".