Students' Use and Perceptions of the Relevance and Quality of Open Textbooks Compared to Traditional Textbooks in Online and Traditional Classroom Environments
Bibliographic record
Abstract
The bulk of previous research on students' perceptions of open educational resources have lacked a control group of students rating traditional textbooks. Moreover, few studies have examined differences in the perceptions of online students and those taking classes in the classroom. A 2 × 2 cross-sectional design was used in which 925 students, assigned either a traditional textbook or an open textbook in either an online or classroom environment, were recruited to complete an online survey. Students assigned open textbooks were almost twice as likely to report using their textbooks, they used them more frequently, and for more time per week overall. Students assigned open textbooks also perceived a greater degree of overlap between the textbook, lecture, and quiz material than did students assigned traditional textbooks. Finally, ratings of the open textbooks were significantly higher than ratings of the traditional textbooks overall and on 11 of 15 different dimensions. Few differences in the online and classroom students were detected, suggesting both groups experienced similar benefits of the open textbooks. These findings demonstrate that replacing traditional textbooks with open textbooks may help to offset some of the financial hardships students face while improving students' engagement and satisfaction with their assigned textbook.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".