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Record W1525308691 · doi:10.26108/adsw-3136

Reading from computer screens vs. reading from paper : effects on children's information retention and comprehension

2002· article· en· W1525308691 on OpenAlex
Matthew A. Kerr

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

VenueAcadiaU-DEV · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsAcadia University
Fundersnot available
KeywordsReading (process)Reading comprehensionComprehensionComputer sciencePsychologyMultimediaLinguistics

Abstract

fetched live from OpenAlex

This study examined whether differences exist between how children read on paper and how they read on computer. Both medium of text presentation and reading instructions were examined. Participants were 60; grade five students, who each read two expository texts, one in each medium. Thirty-Four children were asked to read the text to understand it, while the other 24 were given search questions to guide their reading. Following reading each text, participants were asked to recall as much as they could from what they have read. They were then given questions to cue their recall and to measure their comprehension of the passages. When reading on computer, children took longer to read, and recalled more of the text Material. Reading instructions did not influence performance. When efficiency variables were examined, which take time into account when examining dependent variables, the benefit of computers to recall disappeared. Children were, however, more efficient at comprehension when reading from paper. The results suggest that the disruption caused to reading by the computer presentation system may serve to not only slow young readers, but also hinder their comprehension efficiency.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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