Beyond the net generation debate: A comparison between digital learners in face-to-face and virtual universities
Bibliographic record
Abstract
<p>In the last decade, an important debate about the characteristics of today’s students has arisen due to their intensive experience as users of ICT. The main belief is that frequent use of technologies in everyday life implies competent users able to transfer their digital skills to learning activities. However, empirical studies developed in different countries reveal similar results suggesting that the ‘digital native’ label does not provide evidence of a better use of technology to support learning. The debate has to beyond and focus on the implications of being a learner in a digitalised world. This research is based on the hypothesis that the use of technology to support learning is not related to the fact of belonging or not to the net generation, is mainly influenced by the teaching model.</p><p>The study compares the behaviour and preferences towards ICT use in two groups of university students: face-to-face students and online students. A questionnaire was applied to a sample of university students from five universities with different characteristics (one of them offers online education and four offer face-to-face with LMS teaching-support).</p><p>Findings suggest although access to and use of ICT is widespread, the influence of teaching methodology is very decisive. For academic purposes, students seem to respond to the requirements of their courses, programmes and universities. There is a clear relationship between the students’ perception of usefulness regarding certain ICT resources and the teachers’ suggested uses of technologies. The most highly rated technologies correspond with those proposed by teachers. The study shows how the educational model (face-to-face or online) has a stronger influence on the students’ perception of usefulness regarding ICT support for learning than the fact of being a digital native.</p>
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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 | 0.005 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".