2016 International Conference on Advanced Cloud and Big Data (2016)
Chengdu, Sichuan, China
Aug. 13, 2016 to Aug. 16, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBD.2016.037
It is found that learners prefer to use micro learning mode to conduct learning activities through open educational resources (OERs). However, adaptive micro learning is scarcely supported by current OER platforms. In this paper we focus on profiling an effective micro learning process which is central to establish the raw materials and set up rules for the final adaptive process. This work consists of two parts. First, we conducted an educational data mining and learning analysis study to discover the patterns and rules in micro learning through OER. Then based on its findings, we profiled features of both learners and OERs to reveal the full learning story in order to support the decision making process. Incorporating educational data mining and learning analysis, an cloud-based architecture for Micro Learning as a Service (MLaaS) was designed to integrate all necessary procedures together as a complete service for delivering micro OERs. The MLaaS also provides a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, was also introduced.
Mobile communication, Data mining, Big data, Electronic learning, Mobile handsets, Predictive models
G. Sun, T. Cui, G. Beydoun, J. Shen and S. Chen, "Profiling and Supporting Adaptive Micro Learning on Open Education Resources," 2016 International Conference on Advanced Cloud and Big Data(CBD), Chengdu, Sichuan, China, 2016, pp. 158-163.