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Record W2095939697 · doi:10.2190/ec.38.3.a

Digital Natives, Digital Immigrants: An Analysis of Age and Ict Competency in Teacher Education

2008· article· en· W2095939697 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Educational Computing Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInformation and Communications TechnologyDigital nativeImmigrationCompetence (human resources)Diversity (politics)Digital dividePsychologyMathematics educationPedagogySociologyComputer scienceSocial psychologyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This article examines the intersection of age and ICT (information and communication technology) competency and critiques the “digital natives versus digital immigrants” argument proposed by Prensky (2001a, 2001b). Quantitative analysis was applied to a statistical data set collected in the context of a study with over 2,000 pre-service teachers conducted at the University of British Columbia, Canada, between 2001 and 2004. Findings from this study show that there was not a statistically significant difference with respect to ICT competence among different age groups for either pre-program or post-program surveys. Classroom observations since 2003 in different educational settings in Canada and the United States support this finding. This study implies that the digital divide thought to exist between “native” and “immigrant” users may be misleading, distracting education researchers from more careful consideration of the diversity of ICT users and the nuances of their ICT competencies.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.433
Teacher spread0.378 · 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