Intelligent Assessment of Teaching Quality of Computer Technology Courses in Colleges and Universities Based on ISSA-DRNN

With the continuous development of artificial intelligence technology, its application in the teaching of computer technology courses in colleges and universities is gradually deepened. In this study, the traditional sparrow search algorithm (SSA) is first processed to find the optimal solution, and then the ISSA algorithm is proposed and modeled. Training the deep recurrent neural network algorithm (DRNN) and combining it with the ISSA algorithm resulted in the construction of the teaching wisdom assessment model for the course. Finally, a teaching quality evaluation system for computer courses is constructed and applied to explore changes in teaching quality. The results show that in the assessment of the teaching quality of computer technology courses, the course structure and the teacher’s profile occupy the highest weight, and the weights occupied by these two items are more than 0.7. Secondly, the accuracy of the ISSADRNN model reaches more than 95%, and the maximum error is 0.0284. The average scores of each index system are more than 88. The overall performance is good and close to excellent. The model output’s actual value is within the expected range and has high accuracy. It can be seen that the intelligent assessment model of computer teaching quality proposed in this study has broad application potential and practical value.