Do UOC students fit in the Net Generation profile? An approach to their habits in ICT use
Bibliographic record
Abstract
Some authors have stated that university students born after 1982 have been profoundly influenced by digital technologies, showing different characteristics when compared to previous generations. However, it is worth asking if that is a current observable phenomenon. Are those students born after the 80s really more familiar with ICT tools than those born in previous generations? Do they show different study habits and learning paths? Different research lines (Kennedy, et al., 2010; Bennett, Maton, & Kervin, 2008; Gros, García, & Escofet, 2012) highlight that scientific data is rarely used when discussing this generation’s characteristics; however, none of them have proved in statistical terms that college students do not fit in the Net Generation characteristics and that their habits of ICT use in social and professional activities do not differ from older generations. The international research project, Digital Learners in Higher Education, seeks to develop a sophisticated and evidence-based understanding of university learners in different institutional contexts and the perception of cultures in their use of technology in a social and educational context. Data has been collected from four institutions in Canada and Spain: the British Columbia Institute of Technology, the University of Regina, the Open University of Catalonia (UOC), and the University Rovira i Virgili. In order to develop this project, we used a multi-case study embedded design (Yin, 2009). The UOC’s case is deeply analysed in this paper to affirm that the Net Generation is more speculative than real and that includes students’ perception about this phenomenon, and guidelines are proposed in an eLearning context.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".