The Relational Model Research Of University Teaching Mode And Students’ Ability

With the development of the information age, explosive growth of data has happened in all walks of life, the value of data has been proved in many fields such as business and finance etc. In education, however, the application of data mining technology has only just begun. Education data mining has just started for only five years, and rarely research of such study has been produced. What is more, most of them are limited in predicting students’behavior on campus, or specific to the same major and single teaching mode and compared the teaching result with traditional teaching mode to verify the validity of the new educational mode. But, simply normalizing teaching mode does not conform to the actual situation. While in colleges and universities, the exploration of teaching mode has been taken more and more seriously. However, as to the evaluation of teaching mode, the using methods are still stays on the qualitative analysis which lack of objective data that support.If there is a relationship between teaching mode and the students’ ability training? And what kinds of relations existing between them is of great significance. Therefore, this article try to use intelligent classification algorithm to find the relationship of training mode in university and the student ability.Educational computing is a diversified area of study which is concerned with the design, implementation and assessment of computer-based technologies to enhance educational and training. This paper uses the way of educational computing, to study the influence factors of students’ability by designing the relational model between education model and students’ ability.Fully consider the individual differences between students, this paper based on the students’basic information, which has been described as a vector in the form of schema definition, proposes a Descartes Model that effectively combine teaching mode with a specific course, according to the correlation of curriculum and teaching mode at the same time. Such method can push the process of compute the weight of teaching mode into the algorithm to learning, which not only can avoid the artificial error in the quantitative process of teaching mode, at the same time can also get different teaching mode on the influence of different courses. As a result, the model not only can be used to predict the distribution of the teaching model for all students’ability, at the same time can also give some reference for the students’course chosen according to the specified student individual information.