Theoretical Perspectives of How Digital Natives Learn
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
Marck Prensky, an authority on teaching and learning especially with the aid of Information and Communication Technologies, has referred to 21 st century children born after 1980 as ‘Digital Natives’. This paper reviews literature of leaders in the field to shed some light on theoretical perspectives of how Digital Natives learn and how we can use that knowledge to facilitate learning by Digital Natives. To locate this understanding within the context of general Educational Theory, the paper first presents a brief historical review of the foundational educational theories on how people learn. It then discusses some of the contemporary theories on how Digital Natives learn. Out of these two bodies of knowledge the paper synthesizes an understanding of principles, strategies and practices that we could use to effectively teach Digital Natives and facilitate their learning. It is my hope that this review will help readers develop a deeper understanding of how learners of the digital generation learn and how we can design our pedagogical principles and practices to better meet the needs of the digital learners in our teaching contexts today.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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