An accurate recommendation method for elective courses based on grade prediction

Abstract

The invention discloses an elective course precise recommendation method based on score prediction, which comprises the following steps: S1, establishing a neural network framework according to the depth learning of the named entity recognition and the structured text feature extraction; S2, from the structured description of the syllabus of each course, the expression vector that effectively characterizes the course portrait being extracted, that is, the course portrait vector. The invention respectively studies and establishes a student curriculum performance Precision Prediction Model basedon the in-depth analysis of the teaching syllabus and a Students Curriculum Achievement Precision Prediction Model Based on Collaborative Analysis of Student Similarity; for the first time, the accurate education system based on the analysis of students' curriculum achievement is researched and developed, which realizes the course portrait extraction technology of complex curriculum syllabus. Thecourse portrait of a student can be extracted by integrating the course portrait of multiple courses of a student and combining the course achievement. The course portrait of the student can be extracted. Based on the effective score prediction, the accurate elective course recommendation technology is obtained.

Description

technical field [0001] The invention relates to the technical field of course education, in particular to a method for accurately recommending elective courses based on performance prediction. Background technique [0002] With the progress of society, the amount of knowledge produced by human beings has also greatly expanded. In order to meet the needs of society for talents, colleges and universities have set up various majors / directions, such as artificial intelligence majors, cyberspace security, and data science majors. Even in an existing major, along with the development of the discipline in the professional field, new courses will be added continuously. For example, in the computer application major, the newly opened courses include blockchain technology application, deep learning and its application, Computing application development, etc. In the face of extremely rich courses to be selected, how to recommend courses of interest to students in a targeted manner is ...

AI Technical Summary

Problems solved by technology

[0003] The existing research results of accurate recommendation system for elective courses based on the analysis of students' course performance present the following characteristics: 1) The subject perspective is single and lacks interdisciplinary research; The effective combination of achievements leads to fragmented data and one-sided interpretation; 3) The research is limited to the theoretical level, and it is difficult to transform the research results into practical applications

On the whole, there is a lack of systemic problems, and the comprehensive discussion of dynamic, three-dimensional, and globalization has not been carried out. The research is flat and isolated, and it is impossible to accurately analyze the deep reasons behind the potential problems of students.

Images