Bibliographic record
Abstract
Building Learning Communities in RENA A. PALLOFF AND KEITH PRATT Jossey-Bass Publishers, San Francisco 1999 Paperback: $33.00 ISBN 0-7879-4460-2 The authors of Building Learning Communities in Cyberspace, Rena Palloff and Keith Pratt, describe the book as Effective Strategies for the Online Classroom. For me, it was the end of a three-year quest into research, journals, books, conferences, workshops, and list serves for a body of wisdom on this topic. It clearly and succinctly encapsulates both a solid research-based theoretical foundation and direct application of those theories through a step-by-step guidance process. Palloff is an assistant professor at John F. Kennedy University and has been working extensively in health care, academic settings and addition treatment for over twenty years. Pratt is chair of the Management Information Systems Program, main campus and overseas at Ottawa University in Ottawa, Kansas. Since 1994, PaIIoff and Pratt have been pursuing their own individual career paths and have collaboratively conducted research and training in electronic distance education. Right from the beginning, the authors build a strong case for saying that the successful course does not just modify and present the face-to-face course in a new form. After discussion of how the new paradigm for involves a more active collaborative, constructivist approach, the authors explain that the learning community is the vehicle through which occurs online (p.29). Most contemporary leaders in the field of education agree that the success or failure of an course is largely based on the degree to which the participants sense connectivity and community that allows participants to feel, when they enter a discussion forum in a course site, that they have entered a lively, active conversation. (p.11) What makes this book stand out is that although the authors are in agreement with the significance of this pillar of distance education, they present a well-reasoned, logically sequenced case of theory based upon research and experience so that the reader will share in their depth of understanding. However, just when the reader might agree but be ready to say, That's too much work or That's not my teaching style; the authors offer easily adoptable suggestions, examples and 'doable' tips which any conscientious instructor would find enticing. The book is divided into two parts. Part One: The Learning Community in Cyberspace is an extremely thorough overview of the issues involved at all levels of teaching and outside the classroom. As this section explores definitions, concerns, psychological issues relating to responsibilities, rules, norms, vulnerability, ethics, even time factors, group size, and managing the technology; the authors' teaching area helps add a refreshing dimension by interspersing relevant comments from their students who are analyzing organizational behavior and their community experiences. …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".