基于协同过滤算法的试题智能推荐测评研究

Research on Intelligent Recommendation Evaluation of Test Questions Based on Collaborative Filtering Algorithm

随着智慧教育和数字教学的快速发展,如何快捷准确了解学生对知识点的掌握程度成为智能化教学所要解决的问题.利用改进协同过滤算法构建试题智能推荐测评模型,实现学生试题的智能推荐与答题结果自动评测,并对智能推荐模型进行应用实验.实验结果表明:改进协同过滤算法的命中率最高为89%,平均命中率为15.6%,相较于传统过滤算法,其命中率提高了5.4%;模型能够快速得出成绩结果和得分情况,并根据学生的分数与错误情况,有针对性地为学生提出做题与学习建议;改进协同过滤推荐算法的推荐效果较传统算法有所提升,能有效增加教师对学生知识掌握程度的了解,减少教师工作量,帮助教师提高教学质量.

With the rapid development of smart education and digital teaching,how to quickly and accurately understand students'mastery of knowledge points has become a problem to be solved for intelligent teaching.The research uses the improved collaborative filtering algorithm to construct an intelligent recommendation evaluation model for test questions,realizes the intelligent recommendation of students'test questions and automatic evaluation of answering results,and conducts application experiments on the intelligent recommendation model.The experimental results show that the highest hit rate of the improved collaborative filtering algorithm is 89%,and the average hit rate is 15.6%.Compared with the traditional filtering algorithm,its hit rate is increased by 5.4%.In the practical application test,the model can quickly obtain the results and scores,and according to the students'scores and errors,it can provide students with targeted questions and learning suggestions.The recommendation effect of the improved collaborative filtering recommendation algorithm is higher than that of the traditional algorithm,which can effectively increase teachers'understanding of students'knowledge,reduce teachers'workload,and help teachers improve teaching quality.