Degree Name

Degree of Master of Information Systems and Technology by Research


School of Information Systems and Technology


Since the technology of cloud computing has been widely adopted in many areas, it brings new ideas for promoting mobile learning. Practitioners and researchers are interested in drawing the aid of cloud computing to change the current hosting methods of learning management systems (LMSs) in order to provide more conveniences to education providers, better learning experiences to learners and lower costs to both of them. Hence, a new trend emerges, namely the mobile cloudbased learning.

Although cloud computing helps learners to access online learning contents through commonly used devices, it can be difficult to collaborate in mobile environment, for which there are comparatively less literature showing how to offer mechanisms to enhance teamwork performance. This thesis introduces a novel approach to fill those gaps in research.

Because applications over the cloud are service-oriented, they can interact flexibly and be easily composed to execute sequentially or in parallel to form a workflow. Based on this, we have identified a learning flow, a specification of workflow, embedding the Kolb team learning experience (KTLE). The learning flow is realized by the conjunction of the cloud-hosting LMSs and our newly designed serviceoriented cloud-based system, Teamwork as a Service (TaaS). TaaS works as a thirdparty system to add teamwork-focused functions to current cloud-hosting LMSs, in which five web services are involved. In particular, the Survey Service aims to investigate and evaluate learners’ capabilities, especially in the aspects of Kolb’s learning style (KLS); the Jigsaw Service organizes a three-stage jigsaw classroom over the cloud; the Bulletin Service offers a collaborative editing environment for learners to clarify their task schedules as well as evaluate the difficulties of published tasks in KLS and their preferences; the Inference Service conducts the teamworkenhanced task allocation; the Monitor Service enables the mutual supervision during the in-progress team learning.

To coordinate most learners’ talents and give the more motivation, as the core of TaaS, the Inference Service groups learners into appropriate teams and allocates them to suitable tasks. Utilizing the KLS to refine learners’ 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.

We develop TaaS using PHP+Apache+Mysql, and introduce how to use it, by showing its typical user interfaces. To implement our designed learning flow, a wellknown LMS, Moodle, is chosen to play as the role of cloud-hosting LMS. TaaS and Moodle are finally deployed over the Amazon Elastic Cloud Computing (EC2) infrastructure for working altogether to offer the integrated functions which not only facilitate the learning experience in mobile environment, but also enhance learners’ teamwork performance.