Building Bridges and Breaking Barriers: OER and Active Learning in Mathematics
Bibliographic record
Abstract
This article will discuss how open educational resources and instructional technology are used to support student academic success and continuous faculty pedagogical development, as well as reduce barriers to access at an R1 university. This article uses case examples from two instructors from a Mathematics and Computational Sciences department who are using open educational resources and instructional technology as part of an inclusive active learning pedagogy. The first case study is from an integral calculus course and the second case study is from a discrete mathematics course. The article highlights the role of the educational developer in providing pedagogical and technological support to the faculty. The support the educational developer provides is framed by an inclusive pedagogy that foregrounds access and accessibility. Future considerations provided in the article highlight the need for connections and collaborations supported through a Teaching and Learning Collaboration with an emphasis on active learning, classroom training, and open educational resources to create more pedagogically comprehensive and inclusive learning environments.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".