The purpose of our research unit "Aiebook", is to comprehensively use mathematical statistics, machine learning, data mining and analysis and other technologies and methods to analyze the relevant data of learners' learning behavior based on the ebook learning platform. The research mainly uses quantitative processing and analysis of learning behavior to explore the relationship between learning results and learning content, learning resources, teaching behavior and other variables, and finally achieve the research purposes of prediction, structure discovery, and relationship mining.
A Prediction. We usually use a set of data to predict students' future learning behavior or learning outcomes. For example, prediction can help to know who might fail a class; if a student spent the last half hour working in an online learning environment, through the learning log of the last half hour, prediction can help to know whether s/he mastered the skill to solve the next problem. There are many prediction analysis methods such as Classification, Regression, and Latent Knowledge Estimation.
B Structure Discovery. Structure discovery attempts to find structure, patterns and data points in a set of data without any ground truth or a priori idea of what should be found" (Baker & Inventado, 2014). Clustering, Factor Analysis, Knowledge Inference and Network Analysis are common analysis methods of structure discovery.
C Relationship Mining. Its involves discovering relationships between variables in a dataset, these relationships are seen as rules of data for later use (Bousbia & Knowledge Management & E-Learning, 10(4), 455-468 459 Belamri, 2013). There are many Relationship Mining methods such as "Association rule mining,"" "Correlation mining," "Sequential pattern mining," and "Causal data mining."