Measuring the Impact of an Open Educational Resource and Library e-Resource Adoption Program Using the COUP Framework
Bibliographic record
Abstract
Grant programs that foster the use of open educational resources (OER) significantly reduce undergraduate student spending on textbooks per semester. The Zero-Cost Course Materials (ZCCM) grant program at the University of California, Merced (UC Merced), eliminated text costs and ensured access to course materials by replacing required commercial materials with OER and library licensed e-resources. The present study applies the COUP framework (cost, outcomes, usage, and perceptions) to evaluate the ZCCM program. The ZCCM program resulted in student cost savings and did not negatively impact student outcomes. Students in ZCCM courses demonstrated higher rates of course completion than students enrolled in previous sections. For the outcomes of final course grade, passing with a C− or better, and number of credit hours enrolled in, findings were comparable between the cohorts. Student usage and perception of course materials were gathered using a survey. Though students reported favorable views of zero-cost materials, they reported using them less frequently than commercial texts. This research contributes to a growing body of literature that confirms beneficial cost savings for students using zero-cost materials without jeopardizing students’ success.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".