Book Review - Higher Education through Open and Distance Learning. Editor: Keith Harry
Bibliographic record
Abstract
There is a growing awareness of the important role of open and distance learning in higher education.This awareness has opened the debate around various issues addressed in this text.Where open and distance learning was once seen as an experimental alternative to traditional delivery, new technologies have now made it much more than an experiment.Open and distance learning has grown into a higher education industry on its own and has become one of the main pathways to global education.Keith Harry, the editor, has put together a valuable collection of contributions, giving the reader a true worldview on open and distance learning in higher education.The contributions include the theoretical, the practical, the factual as well as some reflective contemplation on the issues of open and distance learning.The volume does not read very easily because of the variation in styles of writing.It has nevertheless been very well edited and provides the reader with a well-structured and concise source of information.the drive for dual-mode status, technology, and the need to meet the demands of new audiences.These underlying factors are found within the discussions of the various authors, highlighting different aspects of open and distance learning throughout the publication.This book is divided into two parts: Part 1 deals with 5 themes in open and distance learning while Part 2 takes the reader through regions of the globe with case studies of significant open and distance learning institutions presented by various contributors.The regions with their different case studies and authors are:Africa
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.042 |
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".