Mobile learning is an emerging trend
that brings many advantages to distributed learners, enabling them to
achieve collaborative learning, in which the virtual teams are usually
built to engage multiple learners working together towards the same
pedagogical goals in online courses. However, the socio-technical
mechanisms to enhance teamwork performance are lacking. To meet this
gap, we adopt the social computing to affiliate learners' behaviors and
offer them computational choices to build a better collaborative
learning context. Combining the features of the cloud environment, we
have identified a learning flow based on Kolb team learning experience
to realize this approach. Such novel learning flow can be executed by
our newly designed system, Teamwork as a Service (TaaS), in conjunction
with the cloud-hosting learning management systems. Following this
learning flow, learners benefit from the functions provided by
cloud-based services when cooperating in a mobile environment, being
organized into cloud-based teaching strategies namely 'Jigsaw
Classroom', planning and publishing tasks, as well as rationalizing task
allocation and mutual supervision. In particular, we model the social
features related to the collaborative learning activities, and introduce
a genetic algorithm approach to grouping learners into appropriate
teams with two different team formation scenarios. Experimental results
prove our approach is able to facilitate teamwork, while learners'
capabilities and preferences are taken into consideration. In addition,
empirical evaluations have been conducted to show the improvement of
collaborative learning brought by TaaS in real university level courses.

Collaborative learning; Learning flow; Learning styles; Mobile cloud; Social computing; Task allocation

Collaborative learning; Learning flows; Learning Style; Mobile clouds; Social computing; Task allocation