Teaching Evaluation of Electronic Information Science and Technology Based on Data Mining and Visualization Technology

In the new stage of deepening the reform of higher education, as a key factor reflecting the quality of teaching, teaching evaluation has been paid more and more attention. In the face of a large amount of accumulated teaching evaluation information, the traditional evaluation method only stays in the summary and calculation of numerical values, and lacks the mining and analysis of the logical relationship behind the data information. In this regard, based on the current situation of teaching evaluation in colleges and universities, this paper will choose electronic information science and technology as the research object, and propose a set of data-driven teaching evaluation methods to optimize and upgrade the teaching evaluation system in colleges and universities. The whole method takes data mining technology as the core, adopts ID3 decision tree algorithm and Aprioir algorithm to build a teaching evaluation model, and reflects the key factors affecting teaching quality through data collection, data preprocessing, data mining, visual result analysis and other processes. Practice has proved that Aprioir algorithm based on association rules can reflect the degree of students' influence on teaching evaluation under different conditions, and ID3 decision tree algorithm based on classification rules can reflect the key role of teachers in teaching evaluation under different conditions, thus helping universities find out the problems existing in the teaching of electronic information science and technology courses and providing necessary reference for teaching reform.