Towards organizing smart
collaboration and enhancing teamwork performance: a GA-supported system
oriented to mobile learning through cloud-based online course

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At present, people’s
learning styles become diverse, for which mobile learning is a
significant trend that enables distributed learners to achieve
collaborative learning and allows them to be engaged in virtual teams to
work together. In this research, we develop a system to cope with the
problem in current mobile learning circumstance, where learners in
virtual teams generally lack proper communications, guidance and
assistances between each other. Following the theory of Kolb team
learning experience, we use our system, Teamwork as a Service (TaaS), to
work in conjunction with cloud-based learning management systems. This
system targets at organizing a series of learning activities and then
forming a learning flow in order to allow learners to participate into
smart collaborations. Executing the five web services of TaaS
sequentially, learners can have their collaborative learning arranged in
a better environment, where they are able to know about one another, be
grouped into cloud-based ‘Jigsaw Classroom’, plan and publish tasks and
supervise other learners mutually. In particular, one primary point of
enhancing learners’ teamwork performance is to offer them computational
choice of task allocation. For this reason, we model the social features
related to the collaborative learning activities, and introduce a
genetic algorithm (GA) approach to group learners into appropriate teams
with two different team formation scenarios. The technical details of
the operation principle of GA are illustrated thoroughly. Finally,
experimental results are presented to prove our approach is workable to
facilitate teamwork with considerations of learner’s capabilities and
preferences. We also demonstrate our implementation details of the newly
designed TaaS over the Amazon cloud and discuss the main improvements
for collaborative learning brought by TaaS.