Personalized Experiment Push System Based on RIMER Student Ability Assessment Model

Abstract

Experimental courses in engineering majors in colleges and universities are suffering from insufficient class hours, single course experiment contents, and difficulty assessing the students' knowledge mastery. Based on the power supply experiment course as a platform, this paper designs a prototype system that assesses students' mastery of course-related knowledge and provides a personalized experiment push assistant. At the same time, the system can give teachers course feedback in the form of clearer data. This paper first starts with the experimental result data of senior students who have completed the experimental course and constructs the original RIMER student ability assessment model based on the teacher's experience. Secondly, it uses the knowledge graph to record the relevant information of the experiment and the ability information of the students. Finally, through the algorithms of word segmentation, part of speech tagging, and template matching, an experimental push assistant is constructed to help students analyze the weak parts of their knowledge in the form of human-computer interaction and push relevant experiments. The system will record the use of students and provide teachers with necessary curriculum feedback. The practice results show that the accuracy of the system is high, and after using the system, students' knowledge mastery has increased in varying degrees.