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Record W2341977014 · doi:10.5539/ass.v12n5p14

Perceptions and Preferences of Digital and Printed Text and Their Role in Predicting Digital Literacy

2016· article· en· W2341977014 on OpenAlexvenueno aff
Soonhwa Seok, Boaventura DaCosta

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)FluencyLiteracyPsychologyTest (biology)Digital literacyPerceptionMathematics educationMultimediaComputer sciencePedagogyLinguistics

Abstract

fetched live from OpenAlex

<p>This study explored the relationships between the reading of digital versus printed text among 1,206 South Korean high school students in grades 9 through 12. The <em>Test</em><em> of </em><em>Silent Contextual Reading Fluency</em> (2<sup>nd</sup> ed.), the <em>Digital Propensity Index</em>, and the <em>Reading Observation</em> <em>Scale</em> were among the instruments used to measure reading proficiency and digital propensity. Statistical analysis was comprised of a paired sample <em>t</em> test to compare students’ reading perceptions of digital and printed text; independent sample <em>t</em> tests were used to explore reading preferences and the relationships between digital and printed text; and a multiple linear regression was used to explore digital propensity based on reading behaviors. Among the results, students were found to have higher positive perceptions of the reading of printed text; reading preference depended on the purpose for reading (e.g., learning versus entertainment); and significant mean differences were found among students’ reading scores and digital propensity regarding preferences between the reading of digital and printed text. Although much more research is needed before any definitive conclusions can be drawn, the findings suggest several ways to achieve student literacy competency in the use of digital and printed text, while also pointing to additional factors that influence perceptions and behaviors among these two formats.</p>

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 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 score0.648
Threshold uncertainty score0.841

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 studies0.0000.002
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 head0.296
Teacher spread0.283 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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