The Digital Native Debate in Higher Education: A Comparative Analysis of Recent Literature
Bibliographic record
Abstract
More than a decade after Prensky’s influential articulation of digital natives and immigrants,\ndisagreement exists around these characterizations of students and the impact of such notions\nwithin higher education. Perceptions of today’s undergraduate learners as tech-savvy “digital\nnatives” (Prensky, 2001a), who both want and need the latest emerging technologies in all\nlearning situations, continue to dominate the discourse in educational technology research and\npractice. Popular yet controversial conceptions of digital natives continue to be embedded within\nthe assumptions of several contemporary research studies on student perceptions of emerging\ntechnologies, seemingly without regard for a growing body of evidence questioning such\nnotions. In order to promote critical discussion in the higher education community considering\npotential directions for further research of these issues, especially within the Canadian context,\nthe purpose of this review of recent literature is to analyze key themes and issues emerging from\ncontemporary research on the Net generation as digital natives.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.036 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".