Ease of use and usefulness as measures of student experience in a multi-platform e-textbook pilot
Bibliographic record
Abstract
Purpose – The purpose of this paper is to contribute to the understanding of how students accept and use e-textbooks in higher education by assessing their experiences with e-textbooks from Flat World Knowledge (FWK) and Nelson Education during a two year campus pilot. Design/methodology/approach – Students enrolled in one of 11 classes involved in the library’s e-textbook pilot were recruited to complete an online survey including questions related to the perceived usefulness and perceived ease of use of electronic textbooks, as well as their general habits with the textbook. This study uses the Technology Acceptance Model as a framework for analysis. Findings – Students experienced a drop in enthusiasm for e-textbooks from the beginning to the end of the pilot. While research suggests that students prefer for print over electronic in some contexts, students rarely acted on that preference by seeking out available alternative print options. Student experience with the open/affordable textbook (FWK) was very comparable to that of the high cost commercial text (Nelson). Originality/value – While previous research suggests that students have a general preference for textbooks in print rather than electronic, the study suggests that preference may not dictate the likelihood that students will use print options. Students appear to be willing and able to easily make use of the content and functions in their e-textbooks. Despite overall positive reviews for the e-textbooks, students experienced a drop in enthusiasm for e-textbooks from the beginning to the end of the pilot.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".