Traditional learning assessment methods rely on static tests, which cannot reflect students’ learning status in real time. In this paper, we propose an online learning assessment system based on Bayesian network, which can dynamically assess the learning effect of students and update their knowledge status in real time. First, the system collects data from the online learning platform through Python crawler technology, including learners’ test scores, homework scores and learning behavior data. Then, a Bayesian network model is used to model the learning process of the students, assess their knowledge mastery, and calculate the probability of answering questions correctly by combining with IRT theory. Through experimental validation, this system performs well in terms of assessment accuracy and prediction accuracy, with an assessment accuracy of 92.65% and a prediction accuracy of 90.84%. In addition, the system is able to track learners’ behavioral characteristics in real time and improve the effect of personalized teaching by analyzing learners’ learning patterns. The experimental results show that the online learning assessment model based on Bayesian network can effectively improve the accuracy of learning assessment and provide an efficient learning assessment tool for online education platforms.
