Performance evaluation model for operation research teaching based on IoT and Bayesian network technology

The rapid advancement of technology has resulted in significant changes in the field of education. In recent years, the Internet of Things (IoT) and Bayesian network technology have emerged as promising tools for enhancing operations research and teaching performance evaluation. IoT devices and sensors can provide real-time data on student performance, while Bayesian network technology can analyze and predict the complex relationships between various variables. This paper proposes a performance evaluation model based on IoT and Bayesian network technology for operations research teaching. Operations research is a broad discipline with a high level of practicality and applicability. It is widely used in business administration, production planning, traffic management, engineering construction, and financial economics. Using experiments, analysis, and quantification, operations research organizes and manages all types of resources in the system, such as human resources, funds, and goods, and formulates the best business plan. Operations research teaching must combine intuitive geometry and immaterial theorems to improve students’ understanding. The methods used in the teaching stage are simple: the teaching content could be more exciting and unintended, and it is difficult to grasp the teaching focus, making accurate evaluation of teaching performance difficult. Therefore, in the Bayesian network, the incremental results and the performance evaluation model of operations research achieve an accuracy of 87.3%. By thoroughly examining the fundamentals of Bayesian network technology, one can offer technical assistance in building an operational research teaching performance evaluation model. To combine with real-world teaching scenarios to assess the likelihood of risky events occurring during instruction and make a teaching performance evaluation model grounded in operational research. The B5 teaching effect has the highest risk probability of teaching work (0.89), and teachers with high professional titles have higher average values.