Usability evaluation of Ebrary and OverDrive e-book onlinesystems
Bibliographic record
Abstract
Introduction The publishing industry's largest growing sector is e-book sales (Macworld, 2004). For example, e-book sales for the first quarter of 2004 in the USA were up 46%, and e-book revenues were up 28% compared to the same quarter in 2003, according to the Open e-book Forum (2004). One sector likely to benefit from this growth is higher education, because the provision of e-books can be seen as a core feature of integrated e-learning strategies and synergies such as managed learning environments (MLEs) and virtual learning environments (JISC, 2003). Although many problems surround the provision of e-books, such as pricing and licensing ambiguity, budget constraints, and content bias towards the American market (Armstrong et al., 2002), e-books are making inroads into academia. Ebooks are being purchased from individual publishers, and aggregators of e-books, such as ebrary , OverDrive and NetLibrary , already established in the USA, are starting to penetrate the UK market. The latter provide integrated solutions for libraries based on remote-access servers that accumulate collections of e-books provided by different publishers. Although some research has been undertaken investigating the use of aggregators in public libraries (Dearnley et al., 2005) little has been done in the academic sector, particularly with respect to the usability of such services. Some usability research has been undertaken on e-books in general, however. The Visual book experiment, for example, was one of the early attempts to define particular issues concerning the design of usable e-books (Landoni et al., 2000). The experiment revealed the positive implications of visual rhetoric and the book metaphor in e-book design. In addition, the Web project that followed the Visual experiment revealed that ‘scannability’ further improves the reading and overall usability of e-books (Landoni et al., 2000). The EBONI project followed. As part of this project a series of experiments was conducted, for example the Web Book II and the Psychology e-book projects, which confirmed the need for scannability and the book metaphor in the design and usability of e-books (Wilson et al., 2003), and a usability assessment of three online encyclopaedias that revealed the need to integrate web interaction features with the book metaphor (Wilson et al., 2004).
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".