Undergraduate Learning Gains and Learning Efficiency in a Focused Open Education Resource
Bibliographic record
Abstract
The high cost of commercial textbooks in higher education creates barriers to equitable access to learning materials and negatively impacts student performance. Open educational resources (OER) offer a cost-effective alternative, but their impact on student learning remains a critical question. This study directly compared student outcomes between OER and commercial textbooks in a controlled reciprocal design. Forty undergraduate participants completed reading tasks and knowledge assessments using both textbook types, focusing on topics in DNA structure and function and population ecology. Results showed no significant differences in learning gains between OER and commercial textbooks, consistent with prior research. However, participants spent significantly less time on task when using the shorter, learning objective-aligned OER readings, particularly for jargon-heavy DNA content. These findings highlight the potential of OER to reduce cognitive load and improve efficiency without compromising learning outcomes. Future research should explore the role of textbook alignment, length, and student preparation strategies in optimizing learning with OER, particularly in flipped classroom contexts. This study supports OER adoption as a cost-saving measure that maintains academic integrity while enhancing accessibility and efficiency.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".