Interactive E-Texts and Students: A Scoping Review.
Bibliographic record
Abstract
The purpose of this article is to explore the scope of available evidence regarding the use of interactive e-texts and their relationship to student learning experiences in post-secondary education. Following the framework of Arksey and O’Malley, this scoping review identified and reported on 33 articles. Study characteristics are presented alongside four themes that were found across the included articles: (1) the effect of interactive e-texts on student learning experiences; (2) the relationship between interactive e-texts and academic performance; (3) factors influencing student adoption and experience of interactive e-texts; and (4) roles, responsibilities, and recommendations. While the adoption of interactive e-texts is becoming increasingly common in post-secondary education, their effect on student learning experiences remains complex. This review emphasizes the importance of user-friendliness, affordability, accessibility, portability, and the role of educators. Using interactive e-texts shows promise, though future research should explore how barriers might be minimized and benefits might be maximized to have the strongest impact on student learning experiences. Keywords: interactive e-text, student experience, scoping review, post-secondary education
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".