Bibliographic record
Abstract
This article discusses the role of the technologies that have been utilized to advance distance teaching and learning by the National Distance Education University (Universidad Nacional de Educación a Distancia -- UNED) of Spain. Following a description of UNED's historical development and organizational structure, UNED's experience with various educational media is discussed. Printed teaching materials, in the form of didactic units, were one of the first methods to be utilized when UNED began its operations in 1972. In turn, the role of radio and audio recordings, television and video recordings, telephone, videoconferencing, computer systems and computer-mediated communications are also described. UNED's pioneering projects, including the virtual classroom, virtual campus and a program for the physically handicapped, are also detailed. Recent experiments include providing access to radio and television programs on the Internet and adoption of WebCT. On the horizon for UNED are portals for cellular phones using WAP technology and gearing up for multiple applications in accordance with Universal Mobile Telecommunications Technology (UMTS). Lorenzo García Aretio is a Doctor in Educational Science, Professor of Education, and UNESCO Chair in Distance Education at the National Distance Education University (UNED) of Spain. He has also been Director of the University Institute of Distance Education at UNED. As a writer and editor, Lorenzo García Aretio has published 15 books on distance education. He has also written more than 70 articles and chapters for various distance education journals and books.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".