基于知识点与错误率关联的个性化智能组卷模型

大数据环境下的个性化学习模型研究是大规模网络学习环境下的研究热点，本文针对传统的智能组卷策略存在数据训练不足、个性化特点不突出、题库试题知识点分布不均匀等问题，将大数据运用于组卷之中，提出了基于知识点权重与错误率关联的个性化训练模型，优化了抽题的法则并使得个性化特点更精确，在一定程度上有利于学生对薄弱点和盲点的深入理解与消化.本文采用将每章节题目的知识点转化为树形进行管理的方法，并在知识点树中加入知识点错误率元素，来优化基于知识点的抽题结果，研究出适合个人学习情况的个性化模拟练习策略.最后将此新研究模型应用于教学教育系统进行实验研究，研究表明对此关键点的改进更有利于普遍提升学生的整体成绩.

Personalized Intelligent Composition of Test Papers Model Based on Knowledge Point Weight and Error Rate

The research of personalized learning model in big data environment is a hot research topic under large-scale network learning environment. In view of the shortcomings of the traditional intelligent test paper generating strategy, such as the lack of data training, personalized features are not prominent, and the uneven distribution of knowledge points, etc. This study puts forward a personalized practice model, optimizes the rules of paper organization, and makes the individual characteristics more accurate. To a certain extent, it helps student to understand and digest the weak points and blind spot. In this paper, in order to develop a personalized learning practice strategy for personal learning, the knowledge points of each chapter will be transformed into tree management, and add the knowledge point error rate element into the knowledge tree. Finally, this new research model is applied to teaching and education system for experimental research. Research shows that the improvement of this key point is more conducive to improve students'' overall academic achievement in general.