Investigating the Perceptions, Use, and Impact of Open Textbooks: A survey of Post-Secondary Students in British Columbia
Bibliographic record
Abstract
Unrelenting increases in the price of college textbooks have prompted the development and adoption of open textbooks, educational resources that are openly licensed and available to students free of cost. Although several studies have investigated U.S. students’ perceptions and use of open textbooks, there are no published studies of this kind in Canada. Similarly, although the negative impact of commercial textbook costs on student outcomes is well documented within the United States, it is unknown whether these trends generalize to the Canadian post-secondary context. The present study involves a survey of 320 post-secondary students in British Columbia enrolled in courses using an open textbook during the Spring 2015, Summer 2015, and Fall 2015 semesters. The survey investigates students’ textbook purchasing behaviours, including whether, where, and in what format(s) they purchase and access their required textbooks; the negative impact of textbook costs on their course enrolment, persistence, and performance; how they access and use their open textbook, including their format preferences and study habits; and their perceptions of their open textbook, including its quality and what features they like and dislike. The study’s strengths and limitations are discussed, along with recommendations for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".