Digital Natives, Digital Immigrants: An Analysis of Age and Ict Competency in Teacher Education
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".