The Multi-Year Impact of Canada’s First Zero Textbook Cost Initiative
Bibliographic record
Abstract
Kwantlen Polytechnic University (KPU) launched Canada’s first zero textbook cost (ZTC) program in 2018, aiming to alleviate the financial burden of expensive course materials for students. This initiative has since grown to encompass eight credentials and nearly 800 individual courses from which over 24,000 students have benefited from nearly CA$13 million in cost savings. The present study investigated the multi-year impact of KPU’s ZTC initiative on educational outcomes, through an analysis of institutional-level data encompassing 13,605 course sections across the four academic years between September 2018 and August 2022. The study’s pre-registered hypotheses posited that ZTC course sections would exhibit either similar or enhanced levels of student course enrolment, persistence, and performance. Multilevel modelling that controlled for both between-course and within-courses sources of variance showed that ZTC course sections exhibited comparable mean fill and withdrawal rates and slightly higher mean GPAs than non-ZTC course sections. The findings of this study hold significant implications for higher education policy and practice, demonstrating that the substantial student cost savings that result from the programmatic adoption of open educational resources (OER) via institutional ZTC initiatives do not come at the cost of educational outcomes. As post-secondary institutions strive to widen equitable access and better support both pedagogical innovation and student success, the adoption and expansion of ZTC initiatives should be considered as a strategic priority. Limitations, practical recommendations, and directions for future research are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".