Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry (2013) by Norman D. Vaughn, Martha Cleveland-Innes, and D. Randy Garrison
Bibliographic record
Abstract
The advent of the World Wide Web has drastically changed the learning environment for students and instructors both inside and outside the classrooms of today's institutions of higher education.The practice of faculty transferring knowledge to their students solely by in-class lectures and discussions has become out dated.In turn, instructors must come to the realization that the Internet, much like overhead projectors and chalkboards of the past, can now be used as a pedagogical tool to increase the engagement of their students in a "community of inquiry," both inside and outside of today's modern classrooms (p.2).In their book, Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry, Norman D. Vaughan, Martha Cleveland-Innes, and D. Randy Garrison build on these arguments and the previous work done by Garrison and Vaughan in their book Blended Learning in Higher Education (2008).Vaughan, Cleveland-Innes, and Garrison offer instructors a step-by-step guide to how blended learning can increase engagement, interaction, and collaboration in higher education.The ultimate goal of the work is to improve teaching in higher education through the development of blended learning environments that focus on design, facilitation, direction, and assessment.This, in turn, will create and sustain productive communities of inquiry that will benefit everyone engaged in positive learning experiences.Teaching in Blended Learning Environments is not solely a treatise on how 21 st -century instructors must use technology for the betterment of the community of inquiry they are attempting to develop.Nor is this a work that dismisses the instructor as the core of the community of inquiry.Instead, this work goes beyond blending face-to-face learning with the use of technology by positioning the instructor as the designer, facilitator, and director of the blended learning environment.The authors provide readers with a step-by-step instructional manual on how to apply the principles of blended learning in practical settings by combining face-to-face learning strategies with online learning strategies.The book begins with seven principles associated with productive undergraduate teaching, which include: 1. Encourage contact between students and faculty; 2. Develop reciprocity and cooperation among students; 3. Encourage active learning; 518
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".