Abstract
On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.
Highlights
In artificial intelligence research, one of the core issues lies in expressing the existing knowledge and applying the existing knowledge for analysis, processing, or inference in order to obtain new knowledge [1,2,3]
Uncertain knowledge representation can be divided into two categories. e first is a probabilitybased method, including a Bayesian network, dynamic causal network, and Markov network. e second one is a nonprobabilistic method, including fuzzy logic, evidence theory, and rough set theory, among others [6,7,8,9,10]. e Bayesian network was first proposed by Professor Judea Pearl of the University of California in the 1980s [6]
E paper selected 6 requisite courses in computer science as Bayesian network nodes to carry out Bayesian network structure learning and parameter learning. en, taking mathematics course as the evidence node, we carried out dynamic prediction of other course grades. e experimental results show that when mathematics course examination is passed, the probability of passing other courses will increase, which is consistent with the actual values
Summary
One of the core issues lies in expressing the existing knowledge and applying the existing knowledge for analysis, processing, or inference in order to obtain new knowledge [1,2,3]. The expression and inference of uncertain knowledge is the most important and difficult [4, 5]. E Bayesian network was first proposed by Professor Judea Pearl of the University of California in the 1980s [6]. E first is a probabilitybased method, including a Bayesian network, dynamic causal network, and Markov network. He extended the Bayesian network to expert systems and made it a common method for uncertain knowledge and data inference [7]. En, taking mathematics course as the evidence node, we carried out dynamic prediction of other course grades. E paper selected 6 requisite courses in computer science as Bayesian network nodes to carry out Bayesian network structure learning and parameter learning. en, taking mathematics course as the evidence node, we carried out dynamic prediction of other course grades. e experimental results show that when mathematics course examination is passed, the probability of passing other courses will increase, which is consistent with the actual values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
