Bibliographic record
Abstract
This is a practical introduction to blended learning, presenting examples of implementation across a broad spectrum of disciplines.For faculty unfamiliar with this mode of teaching, it illustrates how to address the core challenge of blended learning-to link the activities in each medium so that they reinforce each other to create a single, unified, course-and offers models they can adapt.Francine Glazer and the contributors to this book describe how they integrate a wide range of pedagogical approaches in their blended courses, use groups to build learning communities, and make the online environment attractive to students.They illustrate under what circumstances particular tasks and activities work best online or face-to-face, and when to incorporate synchronous and asynchronous interactions.They introduce the concept of layering the content of courses to appropriately sequence material for beginning and experienced learners, and to ensure that students see both the online and the face-to-face components as being equal in value and devote equal effort to both modalities.The underlying theme of this book is encouraging students to develop the skills to continue learning throughout their lives.By allowing students to take more time and reflect on the course content, blended learning can promote more student engagement and, consequently, deeper learning.It appeals to today's digital natives who are accustomed to using technology to find and share information, communicate, and collaborate, and also enables non-traditional students to juggle their commitments more efficiently and successfully.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".