Are Adult Educators and Learners ‘Digital Immigrants’? Examining the Evidence and Impacts for Continuing Education
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it