Bounded Community: Designing and Facilitating Learning Communities in Formal Courses
Bibliographic record
Abstract
Learning communities can emerge spontaneously when people find common learning goals and pursue projects and tasks together in pursuit of those goals. Bounded learning communities (BLCs) are groups that form within a structured teaching or training setting, typically a course. Unlike spontaneous communities, BLCs develop in direct response to guidance provided by an instructor, supported by a cumulative resource base. This article presents strategies that help learning communities develop within bounded frameworks, particularly online environments. Seven distinguishing features of learning communities are presented. When developing supports for BLCs, teachers should consider their developmental arc, from initial acquaintance and trust-building, through project work and skill development, and concluding with wind-down and dissolution of the community. Teachers contribute to BLCs by establishing a sense of teaching presence, including an atmosphere of trust and reciprocal concern. The article concludes with a discussion of assessment issues and the need for continuing research. A version of this paper was presented at the meeting of the American Educational Research Association (AERA), San Diego, April 2004. Please send inquiries to Brent G. Wilson (brent.wilson@cudenver.edu). [Additional contact information: Brent's phone: 303-556-4363; fax 303-556-4479] Keywords: learning community; instructional design; emergent systems; collaborative learning; teaching presence; sense of community
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".