智能组卷中分级带权重知识点选取策略

HIERARCHICAL WEIGHTED KNOWLEDGE POINTS SELECTION STRATEGY IN INTELLIGENT TEST PAPER GENERATION

针对智能组卷中知识点选取难以满足均匀分布且重点突出的问题,通过建立分级的树形结构知识点管理链表,解决知识点线性管理中知识点密度过大或过小的问题,实现知识点的均匀分布。在分级结构中给出了根据二级权重计算一级权重的数学模型,知识点选取时参考权重,构建了带权重的知识点均匀分布选取算法。算法测试结果表明,所选知识点的相关度低,且各权重知识点的选取比例合理,实现了知识点选取均匀分布且重点突出。

Aiming at the problem of the selection of knowledge points in intelligent test paper generation which is hard to be evenly distributed and stood out, by building the hierarchical tree structure knowledge points management list, we solve the problem in knowledge points linear management that the density of knowledge points is too small or too big, realise the even distribution of knowledge points. In hierarchical structure we present a mathematical model which calculates the grade-one weight according to the grade-two weight, the weight is taken as the reference in knowledge points selection, the even distribution selection algorithm of weighted knowledge points has been constructed. Algorithm testing results show that, the relevance of selected knowledge is low, and the selection proportion of knowledge points weight is reasonable, it realises the even distribution of knowledge points selection with the focuses outstood.