Educational Information Refinement with Application Using Massive-Scale Data Mining

In the manuscript, we propose a novel online learning mechanism based on educational data mining. By leveraging the computer-assisted information-based learning guidance platform, we collect the relevant information of students’ login platform and resource browsing. Subsequently, we preprocess these students’ login data based on which the statistical analysis of students’ login and resource browsing habits are learned through a decision-making mechanism. The decision tree algorithm discovers the underlying factors that influence it from million-scale real-world instructors/students. In this way, instructors and teaching staff can effectively grasp the learning process of students according to the analyzing results. Based on this, the target teaching content integration and teaching model construction can be realized accordingly. This can substantially improve the effectiveness and quality of online learning. In the evaluation stage, we observe that the strategy to deploy a virtual lab environment vigorously brings greater flexibility in the allocation of computing resources to educational institutions. In an ideal sandboxed laboratory context, students can obtain and create an internal network and then have accession to all the computers conveniently. By doing so, gathering savvy skills gives them the workability to build architectures based on mined data. In this work, we adopt the so-called EDUCloud to provide a personal cloud network connection that can flexibly deploy the mined laboratory-related data to facilitate online learning between instructors and students.