How do Virtual Teams Collaborate in Online Learning Tasks in a MOOC?
Bibliographic record
Abstract
Modern learning theories stress the importance of student-centered and self-directed learning. Problem-Based Learning (PBL) supports this by focusing on small group learning centered around authentic problems. PBL, however, usually relies heavily on face-to-face team collaboration and tutor guidance. Yet, when applied in online/blended environments, such elements may not be feasible or even desirable. This study explores how virtual teams collaborate in online learning tasks in the context of a nine-week Massive Open Online Course (MOOC) where international, virtual teams worked on PBL-like tasks. Twenty-one self-formed teams were observed. An inductive thematic analysis resulted in five themes: 1) team formation and team composition, 2) team process (organization and leadership), 3) approach to task work (task division and interaction), 4) use of tools, and 5) external factors (MOOC design and interaction with others). Overall findings revealed that online, virtual teams can collaborate on learning tasks without extensive guidance, but this requires additional communication and technological skills and support. Explicit discussion about group organization and task work, a positive atmosphere, and acceptance of unequal contributions seem to be positive factors. Additional support is required to prepare participants for virtual team work, develop digital literacy, and stimulate more elaborate brainstorming and discussion.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".