Abstract
Open learning environments, such as Massive Open Online Courses (MOOCs), often lack
adequate learner collaboration possibilities; they are also plagued by high levels of drop-out.
Introducing project-based learning (PBL) can enhance learner collaboration and motivation, but PBL
does not easily scale up into MOOCS. To support definition and staffing of projects, team formation
principles and algorithms are introduced to form productive, creative, or learning teams. These use
data on the project and on learner knowledge, personality and preferences. A study was carried out to
validate the principles and the algorithms. The data were provided by students (n=168) and
educational practitioners (n=56). The principles for learning teams and productive teams were
accepted, while the principle for creative teams was not. The algorithms were validated using team
classifying tasks and team ranking tasks. The practitioners classify and rank small productive, creative
and learning teams in accordance with the algorithms, thereby validating the algorithms outcomes.
When team size grows, for practitioners, forming teams quickly becomes complex, as demonstrated by
the increased divergence in ranking and classifying accuracy. Discussion of the results, conclusions,
and directions for future research are provided.
Original language | English |
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Pages (from-to) | 11-20 |
Number of pages | 10 |
Journal | Computers in Human Behavior |
Volume | 45 |
Early online date | 15 Dec 2014 |
DOIs | |
Publication status | Published - Apr 2015 |
Keywords
- Open learning environments
- MOOCs
- Social learning networks
- Team formation model
- Project-based learning
- Project team formation