Bibliographic record
Abstract
A book's title should pull readers in, entice them to visualize its content, and make them want to open the book and flip through its pages.Francine Glazer's Blended Learning does just that.Any instructor who makes his or her living teaching online should welcome a book on blended learning.But don't make the same mistake I did: I didn't read the sub-title, "Across the Disciplines, Across the Academy."I assumed that a book with the words "blended learning" in the title was going to be chock-a-block with facts, with tips and tricks on how to design and deliver blended learning.I was disappointed.Paraphrasing Glazer's words, this book consists of five accomplished academics having frank discussions of pedagogy and the challenges they experienced when transforming their courses from face-to-face to online.It provides a clear appreciation of how these transitions were fraught with multiple challenges in the face of administrative skepticism that could have deterred the most determined instructor from trying new instructional methods.Still, Glazer's 12-page introduction is a must read.It is a superb, concise summation of blended learning and why instructors must pay close attention to how they structure blended courses.These 12 pages provide an explanation of the pedagogical approaches to blended learning that is grounded in the literature.Drawing from the works of over 21 authors, including Randy Garrison, Glazer presents a clear rationale on how well-structured blended courses channel students to be better online learners.If you're looking for a book that details the pedagogy of blended learning, read Glazer's introduction to Blended learning: Across the Disciplines, Across the Academy.If you're looking for a book that tells the story of five academics and their journey to transform face-to-face courses to a blended environment, this may be the book for you.Although Glazer's book is not a just-in-time reference guide for instructing blended courses, each academic's story of transition contains important insights into online pedagogy that inform the novice and expert instructor alike, albeit in a rather circuitous fashion.Contributors use the art of storytelling to explain their experiences of moving to and instructing in a blended environment.While the stories are entertaining, the description of blended learning is thin.This may explain why, in his foreword to the book, James Rhem focuses on the need for
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".