The Adoption of an Open Textbook in a Large Physics Course: An Analysis of Cost, Outcomes, Use, and Perceptions
Bibliographic record
Abstract
Assigning open textbooks in college and university courses can help students save money on increasingly expensive commercial textbooks, and recent research shows that this savings can often be achieved with little to no sacrifice in textbook quality or student learning outcomes. We add to this body of research by examining the use of an open textbook in an introductory physics course at a large research university in Canada that enrols approximately 800-900 students per year. In this course, the instructors revised an open textbook and combined it with other learning resources onto a single website, whereas more than one source of learning materials was used previously. We used the COUP framework to structure our analysis, focusing on cost, outcomes, use, and perceptions in relation to the open textbook assigned in the course. Through the use of a survey of students and data about student learning outcomes in the form of final exam and course grades, and shifts on the pre-/post- Colorado Learning Attitudes about Science Survey, we show that student savings by moving to an open textbook were accompanied by little change in learning outcomes. We also show that the vast majority of survey respondents perceived the open textbook to be of the same or better quality than commercial textbooks used in their other courses. Further, many of them appreciated the fact that the textbook was customized to this particular course—which is made possible by the use of a textbook with an open license.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".