REVIEW: The Handbook Of Blended Learning: Global Perspectives, Local Designs
Bibliographic record
Abstract
Blended learning or blended e-learning sounds like a confusing term at first since it is relatively a new term for today’s instructors. However, Moore reports that it can be traced as far back as the 1920s which was called “supervised correspondence study”. For clarification of the term “blended learning” and informing the instructors about its common practices worldwide, the book provides readers a comprehensive resource about blended learning. It aims to raise awareness of adopting BL from institutional perspectives of many chapter authors from Australia, Korea, Malaysia, the UK, Canada and South Africa who are distinguished people mostly in instructional technology era. With this book, I guess the editors aim at both showing the big picture at macro level and present micro level examples which provide details of blended learning applications among their strengths and weaknesses. As introduced in the book, one of the editors Curtis J. Bonk, a former corporate controller and CPA, is now professor of educational psychology as well as instructional systems technology at Indiana University; the other editor Charles R. Graham is an assistant professor of instructional psychology and technology at Brigham Young University with a focus on technology-mediated teaching and learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".