Academic E-book Usability from the Student’s Perspective
Bibliographic record
Abstract
Abstract Objective – This article describes how librarians systematically compared different e-book platforms to identify which features and design impact usability and user satisfaction. Methods – This study employed task-based usability testing, including the “think-aloud protocol.” Students at the University of Colorado Boulder completed a series of typical tasks to compare the usability and measure user satisfaction with academic e-books. For each title, five students completed the tasks on three e-book platforms: the publisher platform and two aggregators. Thirty-five students evaluated seven titles on nine academic e-book platforms. Results – This study identified each platform’s strengths and weaknesses based on students’ experiences and preferences. The usability tests indicated that students preferred Ebook Central over EBSCO and strongly preferred the aggregators over publisher platforms. Conclusions – Librarians can use student expectations and preferences to guide e-book purchasing decisions. Preferences may vary by institution, but variations in e-book layout and functionality impact students’ ability to successfully complete tasks and influences their affinity for or satisfaction with any given platform. Usability testing is a useful tool for gauging user expectations and identifying preferences for features, functionality, and layout.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".