Reading in the Digital Age: Using Electronic Books as a Teaching Tool for Beginning Readers / La lecture à l’ère numérique: l’utilisation de livres électroniques comme outil d’enseignement pour les lecteurs débutants
Bibliographic record
Abstract
This study stemmed from a concern of the perceived decline in students’ reading motivation after the early years of schooling. This research investigated the effectiveness of online eBooks on eight grade 1 students’ reading motivation. Eight students were given ten 25-minute sessions with the software programs over 15 weeks. Qualitative data were collected from students, teachers, and parents through questionnaires, interviews, observations and field notes. The results suggest the promise of online reading software programs in supporting early readers with reading, motivation, and/or behavioural difficulties. La motivation des lecteurs débutants et les textes qu’ils choisissent de lire ont un impact sur leur succès en littératie et sur leur volonté de prendre part à des activités de lecture au cours des années du primaire. Cette étude s’est penchée sur les expériences de lecture de livres électroniques de huit élèves de première année. Huit élèves ont reçu 10 séances de 25 minutes avec les logiciels sur une période de 15 semaines. Des données qualitatives ont été recueillies auprès des élèves, des enseignants et des parents par l’entremise de questionnaires, d’entrevues, d’observations et de notes. Les résultats suggèrent que les livres électroniques sont prometteurs pour stimuler la motivation des lecteurs débutants.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".