Overcoming Textbook Access Barriers in an Introductory Psychology Course: An OER Study at a Hispanic-Serving Institution
Bibliographic record
Abstract
The high cost of college textbooks is an access barrier for students to overcome during their pursuit of a college degree. Perhaps most at risk are community college students, an older, more diverse, and lower-income population in comparison with their university peers. Recently, community colleges have considered replacing traditional, commercially produced textbooks with free open educational resources (OERs). In this work, two aims are addressed. First, a small-scale investigation of the need for a low-cost textbook alternative was conducted in an introductory psychology course. In response to the finding that over a quarter of students could not afford the course textbook, a psychology OER was adapted from existing resources and piloted in three sections of this course. The second aim was to assess the impact of this OER textbook. Findings from this second survey found that the psychology OER was easy to use, was high quality, and supported students in their understanding of course content. Students also reported that the money saved from not having to buy a textbook made taking the course easier. Together, these findings support that OER textbooks are suitable replacements that can reduce the financial burden on low-income students and support them in the achievement of their academic goals.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".