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

Conference Paper


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Abstract


  • 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.

Authors


  •   Sun, Geng
  •   Shen, Jun
  •   Luo, Junzhou (external author)
  •   Yong, Jianming (external author)

Publication Date


  • 2013

Citation


  • Sun, G., Shen, J., Luo, J. & Yong, J. (2013). Evaluations of heuristic algorithms for teamwork-enhanced task allocation in mobile cloud-based learning. International Conference on Computer Supported Cooperative Work in Design (pp. 299-304). Australia: IEEE.

Scopus Eid


  • 2-s2.0-84884149403

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2209&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1200

Has Global Citation Frequency


Start Page


  • 299

End Page


  • 304

Place Of Publication


  • Australia

Abstract


  • 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.

Authors


  •   Sun, Geng
  •   Shen, Jun
  •   Luo, Junzhou (external author)
  •   Yong, Jianming (external author)

Publication Date


  • 2013

Citation


  • Sun, G., Shen, J., Luo, J. & Yong, J. (2013). Evaluations of heuristic algorithms for teamwork-enhanced task allocation in mobile cloud-based learning. International Conference on Computer Supported Cooperative Work in Design (pp. 299-304). Australia: IEEE.

Scopus Eid


  • 2-s2.0-84884149403

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2209&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1200

Has Global Citation Frequency


Start Page


  • 299

End Page


  • 304

Place Of Publication


  • Australia