一种基于遗传算法的试题推荐方法

A Kind of Test Question Recommendation Method Based on Genetic Algorithm

针对大量试题造成信息过载,导致试题推荐的个性化程度不高、效率低下等问题,根据认知诊断、数据挖掘及自然语言处理等交叉领域的研究,提出一种基于遗传算法的试题推荐方法TCEGA。该方法根据认知诊断模型确定试题与知识点的关联状况,对学生的试题掌握水平进行建模;结合隐含语义分析方法对试题库的数据进行处理,根据试题难度为学生推荐相应的试题。TCEGA考虑了受推荐学生在学习方面的个性,同时考虑了群组学生在学习方面的共性,以提高试题推荐的合理性与准确性。对比实验结果表明,该方法在试题推荐时的准确率达到90.17%,相比传统SOM算法的准确率提高了11.79%,可广泛应用于在线学习的试题推荐场景。

Due to the information overload caused by a large number of test questions,the personalized degree of test question recommendation is not high and the efficiency is low.According to the research of cognitive diagnosis,data mining and natural language processing,this paper proposes a test question recommendation method TCEGA based on genetic algorithm.This method determines the relationship between test questions and knowledge points according to the cognitive diagnosis model,and models the students′mastery level of test questions;Combined with the implicit semantic analysis,the data of the test database is processed,and the corresponding test questions are recommended by the difficulty of the test questions.TCEGA takes into account the individuality of the recommended students in learning and the generality of the group students in learning,so as to improve the rationality and accuracy of the recommendation.Finally,the comparative experiments show that the accuracy of the model is 90.17%,which is 11.79%higher than that of the traditional SOM algorithm,therefore,the method can be widely used in online learning application scenarios.