Evaluations of heuristic algorithms for teamwork-enhanced task allocation in mobile cloud-based learning

Enhancing teamwork performance is a significant issue in mobile cloud-based learning. We introduce a service oriented system, Teamwork as a Service (TaaS), to realize a new approach for enhancing teamwork performance in the mobile cloud environment. To coordinate most learners´ talents and give them more motivation, an appropriate task allocation is necessary. Utilizing the Kolb´s learning style (KLS) to refine learner´s capabilities, and combining their preferences and tasks´ difficulties, we formally describe this problem as a constraint optimization model. Two heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA), are employed to tackle the teamwork-enhanced task allocation, and their performances are compared respectively. Having faster running speed, the SA is recommended to be adopted in the real implementation of TaaS and future development.