Construction of Smart Higher Education Teaching Resources Using Data Analysis Technology in Unbalanced Data Environment

Abstract

The difficulty in gathering teaching resources presents challenges in the process of developing instructional materials for smart higher education. This essay makes a research proposal for a study using data mining technology to create instructional materials for smart higher education. The analysis of the dynamic scheduling mechanism of intelligent higher education teaching resources based on data analysis technology in unbalanced data environment follows research on the establishment of teaching materials from the discovery of teaching materials, the marking of teaching materials, and the organization of teaching materials. In the end, it is determined that class A students' grades are unquestionably higher than those of class B students. Of course, there are some class B students who score higher than average, but class B students tend to score between 50 and 60 points on average, whereas class A students tend to score higher than average. The contrast is greater, and there are more pupils scoring between 90 and 100. The average grade for students in class A is 80.125, whereas the average grade for students in class B is 71.45. The lowest score in Class B is 51, the lowest score in A is 58, and the greatest score in A is up to 98. It is clear that the development of intelligent teaching resources for higher education based on data mining technology is very successful and has been thoroughly proven.