Are Adult Educators and Learners ‘Digital Immigrants’? Examining the Evidence and Impacts for Continuing Education
Bibliographic record
Abstract
Over the past decade, Prensky’s distinctions between “digital immigrants” and “digital natives” have been oft-referenced. Much has been written about digital native students as a part of the Net generation or as Millennials. However, little work fully considers the impact of digital immigrant discourse within the fields of adult learning and continuing education. It is promising that rather than being digitally challenged immigrants for whom new learning technologies are completely foreign, adults of different ages can bring valuable knowledge and skills to e-learning environments that enable them to achieve academic success. These are important findings, since e-learning is increasingly recognized as an important part of learning across the life-course. With the growing body of research evidence countering common digital native and immigrant distinctions and critiquing an underlying technological determinism informing such arguments, how might practitioners respond to these discourses in their own educational contexts? With a focus on digital immigrants, the purpose of this article is to provide critical consideration of current research evidence on digital native/immigrant distinctions that impact educators and learners within the field of continuing education.
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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 | 0.028 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".