An Investigation of a Customizable Entertaining Animated E-Book: A Gender Difference Perspective
Bibliographic record
Abstract
Game-based learning, electronic books (e-books), and animations offer different advantages and serve distinct purposes. Consequently, this study aimed to propose an entertaining animated e-book by seamlessly integrating these three information technologies. Additionally, customization was incorporated into the entertaining animated e-book to accommodate learners’ diverse preferences. In other words, a Customizable Entertaining Animated E-book (CEAE) was implemented in this study, which also aimed to investigate the influences of gender differences on their reactions to the CEAE. Results indicated that the CEAE could reduce gender differences, in terms of test performance and task performance. However, differences between males and females still existed in learning behavior and gaming behavior. More specifically, males and females preferred to use different scaffolding hints and choose different gaming modes. Based on these findings, we introduced a framework, which could work as a valuable reference for instructors to implement e-books, GBL, and animations in educational settings. This framework could also provide guidelines for designers to personalize entertaining animated e-books.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".