Book Review – Making the Transition to E- Learning: Strategies and issues
Bibliographic record
Abstract
While reading this interesting book, I had a big question lingering in the back of my mind. Should we be concerned at all about the “transition to e-learning”? Should we not make effective use of e-learning in both distance and non-distance education contexts? Having read the book, I can say emphatically that the editors – and most of the authors of the 20 chapters that constitute this book – believe in ‘effectiveness ’ if not exactly the ‘transition ’ to e-learning. After all, making a transition makes us move from one place to another; and in this case, we do not leave behind either the face-to-face teaching or traditional distance education. In the preface to the book, the editors make it clear that e-learning is being used “without a solid understanding of how to plan and develop instruction, of underlying teaching and learning theories, and of what makes the Internet a unique medium for teaching and learning ” (p. viii). In order to address this gap, the editors successfully pulled together an experienced group of teachers and researchers from five different countries to contribute on pedagogical implications of new technology. Of the 20 contributions, however, only five come from outside of Canada; and thus the book is highly Canada-centric. In spite of this, there are enough good lessons to be learned for all of us in this book. Initially, the editors take on clarification of the meaning of e-learning, which fall under
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".