Bibliographic record
Abstract
As a new college instructor, I am always looking for innovative ways to motivate my students and encourage them to collaborate with their peers.I am not alone.According to a recent Horizon Report (2008) a "renewed emphasis on collaborative learning is pushing the educational community to develop new forms of interaction" (p.5).The incorporation of Web 2.0 technologies into educational settings is also changing the way we think about teaching and learning by enabling students to access courses and materials anytime, anyplace.For example, Webware suites, such as Google.docsand even virtual worlds, like Second Life, can be used to support collaborative learning both in and out of the classroom.Because of these technological advances, places like the local café off-campus or the hallway areas outside the departmental offices are more than merely social gathering areas; they are also becoming educational spaces.While teaching and learning are no longer restricted to the formal settings, this does not mean that we should ignore classroom-based models.According to John Seely Brown and Richard Adler (2008), social learning areas, including virtual worlds, can "coexit with and expand traditional education" (p.22).Beginning with non-traditional settings, the selections in the book, Online Collaborative Learning: Theory and Practice, explore the social learning concept and examine different ways to incorporate this approach into the curriculum.These chapters are written by a diverse group of academics who represent countries such as Australia, Canada, Denmark, Germany, and the United States.Overseeing this collection is Tim Roberts, a Senior Lecturer at the Central Queensland University in Australia.Not only does this editor have experience teaching thousands of students in locations around the world -many who are studying topics completely online -but he has won awards for his research.Thus, Roberts' experiences as a practitioner and a researcher are adequate qualifications for bringing together these works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".