Site evaluation report: J. Baggaley, 2003 THE EVALUATION AND SELECTION OF COLLABORATIVE TOOLS IN DISTANCE EDUCATION
Bibliographic record
Abstract
Distance educators and their students benefit greatly from the use of online collaborative techniques. In the past two years, the traditional text-based conferencing methods have been complemented by audio/video-conferencing methods, whiteboard, polling, and instant messaging techniques. These operate in synchronous and asynchronous communication modes, on a variety of computer platforms, and in stand-alone and integrated designs. For the online teacher, the selection of cost-effective collaborative techniques is complicated by the wide range of competing software methods currently available. At Canada’s Athabasca University, an ongoing software evaluation initiative has been established, providing updated reviews of the collaborative software options, and of the features that teachers and students find most useful. This paper presents the main conclusions of this project during its first year (2002-03). One hundred software products and services have been evaluated to date, and the most promising ones are being applied in the University’s teaching. The paper discusses the range of approaches, and the evaluative criteria used by their graduate student reviewers. Problems of cost, complexity, control, clarity of usage are identified; and the potential of the new online methods for distance education is discussed.
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 | 0.059 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.039 |
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".