Bibliographic record
Abstract
En el discurso de los usuarios habituales de las nuevas tecnologías se percibe un llamamiento urgente que sugiere que los estudiantes de hoy en día no están muy satisfechos con los modos tradicionales de aprendizaje. Nuestra investigación, llevada a cabo en seis países diferentes y en diversas instituciones, sugiere que el estudio de la aplicación de las nuevas tecnologías en el aprendizaje debe estar enfocado, más que a los usuarios habituales, a los principiantes en la utilización de estas herramientas. Aunque algunas experiencias claramente muestren un aumento del uso de las tecnologías digitales, más pronunciado en los jóvenes que en los adultos, esto no significa que los resultados publicados al respecto estén determinados por la edad. Además, las implicaciones que tiene para la educación están lejos de ser claras. Es hora de que el debate evolucione más allá de la dicotomía simplista usuarios habituales de las nuevas tecnologías y recién iniciados en las mismas. Nuestra investigación muestra que los nuevos usuarios, independientemente de la edad, comparten las siguientes características: poseen las habilidades que estas herramientas requieren, tienen acceso a ellas, saben cómo utilizarlas y conocen sus beneficios. Lo que les diferencia son los puntos de vista sobre cómo integrar los usos sociales y los académicos. Generalmente no contradicen el paradigma académico dominante. AbstractThere is a sense of urgency in the digital natives discourse that suggests today’s learners are becoming impatient with traditional modes of teaching because they have grown up digitally. Our research and research conducted in six different countries and at a range of different institutions suggests we need to be focusing on digital learners, not digital natives. While the empirical evidence clearly shows the use of digital technology is growing, and young people tend to use it more than older people, it just as clearly shows that the issues are not defined by generation and the implications for education are far from clear. It is time to move beyond the simplistic dichotomy of digital natives and digital immigrants. Our research shows today’s learners, regardless of age, are on a continuum of technological access, skill, use and comfort. They have differing views about the integration of social and academic uses and are not generally challenging the dominant academic paradigm.
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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".