Open to Open? An Exploration of Textbook Preferences and Strategies to Offset Textbook Costs for Online Versus On-Campus Students
Bibliographic record
Abstract
As open textbook initiatives are on the rise, a burgeoning literature has begun exploring student perceptions of openly licensed textbooks used in higher education. Most of this research has lacked consideration of potential differences in the perceptions of online and on-campus students and has failed to include a control group of students using traditional textbooks. Therefore, the authors employed a 2 x 2 design to directly compare perceptions of online students with on-campus students assigned either open or traditional textbooks. Students (N = 925) enrolled in multiple sections of psychology courses at a midsized R1 institution completed a survey on their perceptions of their particular book’s format and features, as well as strategies they typically employ to offset the cost of expensive course materials. The results revealed that online and on-campus students report disparate strategies for offsetting the high costs of textbooks, different preferences in textbook formats (print versus digital versus both) when cost is not a factor, and differences in their ratings of the importance of various textbook features. Moreover, the results indicate that the use of open textbooks may increase preference for free digital textbooks over paid printed textbooks. Based on these results, the authors suggest that campuses might consider providing customized support to different student populations as open textbook initiatives gain in popularity on university campuses. Additionally, they suggest that prior exposure to open textbooks may increase students’ willingness to use openly licensed materials in future courses. They recommend future research on this question, using a longitudinal within-subjects designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".