e-Textbooks Usage by Students at Andrews University: A Study of Attitudes, Perceptions, and Behaviors
Bibliographic record
Abstract
Although e-books have been incorporated into the academic library’s collection for over a decade now, it has not been, until today, without question. It is still, as the literature reveals, a controversial topic for librarians, publishers, and users around the globe. Although many researches indicate that patrons still prefer the printed format over the electronic version, the tipping point seems to be reaching us earlier then many might think. The growing availability of e-books to users has begun to affect user perceptions and attitudes, creating more access and usage opportunities, a recent research concluded. This paper presents the results of a large scale survey designed to investigate usage patterns of and attitudes towards e-books by students at Andrews University. One important aspect which the study investigated is how the use of e-books impacts student’s learning. The subjects were divided into two different groups, namely, (1) students who purchased the electronic version of an e-textbook for a class (the bookstore offered 74 books in an electronic format), and (2) students who had the opportunity of purchasing the electronic version of a textbook but preferred the traditional print format. Only four percent of the population studied opted to use an e-textbook. The print version is still greatly preferred by college students. However, the majority of those who used e-textbooks, would use it again and would recommend it to a friend. Lack of awareness, not knowing how to get it, eyestrain, and difficulty of reading are the culprits for students not using e-books more often. Although it is possible to note an increase of e-books usage, caution is recommended when developing collection development policies when includes e-books.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".