An Effective Cloud-Based Simulator Facilitating Learning Analytics on Mobile Devices
Learning analytics is targeted to better understand each learner’s interests and unique characteristics in learning so as to build a personalized learning environment. However, many learning analytics techniques may require relatively intensive computation, thus inappropriate for any mobile application. In this paper, we propose an interactive and personalized e-learning system named the COMPAD+ simulator facilitated by an intelligent learning analytics algorithm running on the cloud server to quickly estimate the learner’s areas of interests on the simulation results as based on his/her initial inputs and then flexibly generate the simulation details as appropriate. To protect the data privacy of each individual learner, the personal data is stored in the learner profile under each password-protected account on the cloud server with all the intermediate simulation data to be erased after each learning task. Up to our understanding, this work represents the first attempt to successfully develop a flexible, interest-based, and platform-independent simulator directly run on any mobile device facilitated by an efficient learning analytics algorithm working on the cloud server. To demonstrate its feasibility, a prototype of our cloud-based COMPAD+ e-learning system is built and carefully evaluated on various mobile devices. Clearly, there are many promising directions in terms of both pedagogical and technological impacts to extend and enhance our interest-based COMPAD+ simulation platform for future e-learning systems.
The authors are deeply grateful to Dr. Daniel Churchill, Dr. Kinshuk, and Professor Yi Shang for their fruitful discussions and valuable inputs on learning analytics and mobile learning.
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