Bibliographic record
Abstract
Certain researchers suggest that a teaching method which encapsulates collaboration and interaction would likely work well within the framework of Internet based distance education courses. In order to verify how interaction and collaboration work between students in distance education courses, we observed and researched two undergraduate university courses offered via the Internet. All communications were text based and in asynchronous mode relying upon e-mail, group discussions and hypertext navigation to facilitate the collaborative work process. This exploratory study has enabled us to identify some problematic elements which can hamper collaboration between distance education students. For example, even though most of the distance education students enjoyed communicating with one another, with the professor and with the teaching assistant, the collaborative learning assignments were not always a successful endeavour. To begin with, some students complained of technical difficulties which greatly hampered communication, interaction and collaboration with distant partners. Other students, who had a good technical knowledge of the Internet, had trouble developing a working relationship with out of province or overseas partners. Another interesting observation was the fact that some autonomous, highly independent students preferred working alone. They felt that the collaborative tasks placed undue constraints on their personal work schedule. From these results, we developed recommendations for ensuring the success of collaborative assignments for future Internet courses.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".