The Learning Preferences of Digital Learners in K-12 Schools in China
Bibliographic record
Abstract
Background:Students grown up with digital technology and Internet are called digital natives or net generation. All others, who grew up without so much immersion with digital technologies are called digital immigrants. Researchers held different ideas on whether a new generation of learners existed. The foci of the debate is whether taking age as the main symbol to divide “digital native” and “digital immigrants”.Material and methods:This paper presents the analysis of the debate and on that basis hypothesizes that the time length for using technology could be used as the criteria for digital learners. In order to test the hypothesis and understand learners’ learning preference, a large-scale survey with 44470 participants and 7 focus group interviews were conducted.Results:Results showed NetizenYears could be used as the criteria of digital learners, and non-digital learners and digital learners had significantly different Internet use patterns. More positive attitudes to Internet, more active participation online and more tendency to Internet addiction were found for digital learners with NetizenYears increasing.Conclusions:The gap between digital learner’s preferred learning approach and teaching methods in classroom was discussed. The paper concludes with a discussion on using the time length of using technology as the criteria for digital learners.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".