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Record W4399782238 · doi:10.1080/10447318.2024.2364467

An Investigation of a Customizable Entertaining Animated E-Book: A Gender Difference Perspective

2024· article· en· W4399782238 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2024
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsPerspective (graphical)Visual artsArtMultimediaComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.315
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it