Quality of learning analysis based on Bayesian Network

The level of quality of learning is directly related to the competitiveness of students in the future social life, the overall quality and comprehensive national strength of Chinese citizens; the establishment and improvement of student learning quality analysis and guiding systems are the strategic starting point for promotion of education. The Bayesian Network (BN) proposed by Pearl is a new mechanism for uncertain knowledge representation and manipulation based on probability theory and graph theory. BN is network structure with clarity semantics. It exploits the structure of the domain to allow a compact representation of complex joint probability distribution. Its sound probabilistic semantics explicit encoding of relevance relationships, inference algorithms and learning algorithms that are fairly efficient and effective in practice, and decision-making mechanism of facility, have led BN to enter the Artificial Intelligence(AI) mainstream. The present thesis is to make an experimental analysis of the test paper based on Bayesian Network. The main toolkit used in this experiment is BNT software suite compiled with MATLAB. This software suite provides us with a lot of basic function sets for Bayes Network learning. It is suitable for the accurate and appropriate logics of various types of joints, and it also has the function of parameter learning and structure learning. From the experiment we come to the conclusion that five factors including “absorption rate of teaching” and “work accuracy” have great influence on quality of learning.