Journey to textbook affordability: An investigation of students’ use of eTextbooks at multiple campuses
Bibliographic record
Abstract
eTextbooks have steadily and recently more rapidly penetrated the textbook market. In order to effectively support students’ learning, it is important to comprehend students’ experiences using eTextbooks. This survey study was designed to gain an understanding of students’ experiences in using eTextbooks and variables that impact their experiences, perceptions, and attitudes towards eTextbooks. In a total of 33 courses, faculty members at five state university campuses in California participated in the eTextbook pilot project during the fall of 2010. Six hundred and sixty-two student questionnaires were returned from those courses. Key findings include: 1) More than one-third of the students were satisfied with the eTextbook; 2) more than half of the students felt that the eTextbook was easy-to-use; 3) older students (22 or older) tended to have more positive experiences with the eTextbook than younger students; and 4) students most liked the eTextbook’s cost, accessibility, light weight, and keyword search features. This study implies that the eTextbook must be a high-quality, easy-to-use resource to serve as a viable textbook option for student learning.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".