Assessing the Savings from Open Educational Resources on Student Academic Goals
Bibliographic record
Abstract
Our study found that most students considered OER to be as good or better in terms of quality and engagement as traditional texts, while also allowing them to put saved funds toward their educational pursuits. As rising costs in higher education affect current and potential students, faculty and students are looking for ways to cut costs where possible. Open educational resources (OER) are a viable option to replace expensive traditional textbooks without sacrificing quality. This article presents the results of a study conducted with students at a Virginia community college who took courses that used OER. At the end of the semester, students were asked to rate their perceptions of the OER quality and their level of engagement with OER as compared to traditional textbooks. Results indicate that a majority of students found the OER to be as good as or better than traditional textbooks in both quality and engagement. While similar studies have been conducted, this study also asked students to briefly describe how they used the money saved by not having to purchase a textbook. Many students indicated they used the money to reinvest in their education by paying tuition, purchasing materials for other courses, or taking additional courses; day-to-day expenses and savings were the next most common responses. Further research needs to be conducted to understand the effect these savings and reinvestment have on students’ completion of academic goals.
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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 | 0.003 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".