E-Learning with visual analytics

With the development of science and technique, E-Learning is used widely in education. A large set of detail information brought by E-Learning can be analyzed to find questions in education, but analytics is hard to be done because of big data. To solve this problem, this paper introduces visual analytics into E-Learning, including a group analysis component(GAC) and a case analysis component(CAC). GAC helps instructors to seek key collective factors of education process layer by layer. Some interesting rules are found: (a)The submission number is proportional to the score; (b) The better Average Submission Number(ASN) should be around 2.0; (c) Students with low ASN have more likelyhood of plagiarism;(d) Students with high ASN should be paid more attention. When analytics are processed from entirety to locality, outlier students are found, CAC can be used to get further observation in detail, and the educational strategies should be adjusted for these outliers. Visual analytics of E-Learning can help instructors to improve education quality and create a practical educational mode with a clear insight of the whole process.