Bibliographic record
Abstract
This e-book is a report of a study, which is supported by the European Union Leonardo da Vinci programme, about the potential of wireless technologies for teaching/learning at a distance.Despite its focus on the wireless e-learning scenario in Europe, this report does bring to the fore the pedagogic feasibility of using wireless technologies in distance teaching/learning contexts beyond Europe.Of the 10 chapters that form this report, the first three attempt to provide theoretical scaffolding to the discussion of mobile learning in Chapter 4. Chapters 5 to 8 contain a compendious account and a tenuous analysis of four wireless devices used for creating mobile learning contexts.Chapter 9 describes student perceptions of and experience with mobile learning.Building on the foregoing discussion, Chapter 10 urges Europe to take up the leadership role, because it forecasts the inevitability of adapting to mobile learning environments, consequent on the growth of wireless technologies and, indeed, a wireless future.This 172-page report defines distance learning ("d-learning") as essentially learning through print, e-learning as electronic learning in wired environments, and m-learning as wireless elearning.This report also claims "to provide an analytical and theoretical background to the field of education and training provision known as mobile learning...[a] provision of education and training courses on wireless devices... [and] sees the provision of education at a distance as a continuum and traces an evolution from d-learning (distance learning) to e-learning (electronic learning) to m-learning (mobile learning) . .." (p. 6).Chapter 1 (p.8 -17), 'The future of learning,' describes the close nexus between technologies and educational environments, and in that context outlines how industrial and electronic revolutions have paved the way for traditional distance education, the mainstays of which is the print medium and electronic learning (e-learning), respectively.This Chapter describes how the evolution of mobile technologies "will change the distance student from a citizen who chooses not to go to college, to a person who not only chooses not to go to college, but is moving at a distance from the college" (p.11).The emerging challenge, as a consequence, is to build appropriate learning systems for "wireless computing and telephony" in the same way "as eLearning has provided for wired computing and telephony" (p.16).Chapter 2 (p.18 -30) entitled 'From d-Learning to e-Learning' describes distance education, and discusses its two forms and traces its history.The author uses the expression "d-Learning" to refer to both distance learning (p.18) and teaching at a distance (p.19).He then makes a
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".