With the popularity of English, more and more attention has been paid to students’ Englishlearning. In order to understand student learning and make accurate predictions about studentperformance, this paper analyzed ...With the popularity of English, more and more attention has been paid to students’ Englishlearning. In order to understand student learning and make accurate predictions about studentperformance, this paper analyzed student performance under English teaching by using adecision tree algorithm, i.e. the C4.5 algorithm. The calculation process of the algorithm was simplifiedby the Taylor series, and an example was analyzed. The results showed that the runningtime of the improved C4.5 algorithm was improved by 22.86% compared with the C4.5 algorithm,the precision rate was above 75%, the recall rate was above 85%, and the F1-measure value wasabove 80%. The experimental results verified the effectiveness of the improved C4.5 method instudying student achievement. This work is beneficial to the further optimization of decision treealgorithms and provides some reference for the application of intelligent algorithms in the fieldof education.展开更多
The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the cons...The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the construction of English teaching resources database, a method of optimizing the allocation of English teaching resources is proposed based on network cloud platform. Text semantic key words conceptual decision tree model is constructed for massive English teaching resources allocation, semantic information conversion method is used to compute key semantic features of English Teaching resources, the concept convergence point of English Teaching resource allocation is formed in semantic model. According to the set between the upper and lower relationship, a decision tree model of English Teaching semantic subject words is constructed, semantic conversion and information extraction are realized. English teaching resources optimization allocation simulation is taken in the cloud platform, simulation results show that the scheduling performance of English teaching resources is better, and the adaptive allocation ability of English teaching resources is stronger, and the resource utilization rate is higher.展开更多
文摘With the popularity of English, more and more attention has been paid to students’ Englishlearning. In order to understand student learning and make accurate predictions about studentperformance, this paper analyzed student performance under English teaching by using adecision tree algorithm, i.e. the C4.5 algorithm. The calculation process of the algorithm was simplifiedby the Taylor series, and an example was analyzed. The results showed that the runningtime of the improved C4.5 algorithm was improved by 22.86% compared with the C4.5 algorithm,the precision rate was above 75%, the recall rate was above 85%, and the F1-measure value wasabove 80%. The experimental results verified the effectiveness of the improved C4.5 method instudying student achievement. This work is beneficial to the further optimization of decision treealgorithms and provides some reference for the application of intelligent algorithms in the fieldof education.
文摘The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the construction of English teaching resources database, a method of optimizing the allocation of English teaching resources is proposed based on network cloud platform. Text semantic key words conceptual decision tree model is constructed for massive English teaching resources allocation, semantic information conversion method is used to compute key semantic features of English Teaching resources, the concept convergence point of English Teaching resource allocation is formed in semantic model. According to the set between the upper and lower relationship, a decision tree model of English Teaching semantic subject words is constructed, semantic conversion and information extraction are realized. English teaching resources optimization allocation simulation is taken in the cloud platform, simulation results show that the scheduling performance of English teaching resources is better, and the adaptive allocation ability of English teaching resources is stronger, and the resource utilization rate is higher.
文摘常规线上教学资源分类管理系统使用Executor执行器分配教学资源,易受执行器输入输出接口参数影响,导致分类管理系统的运行状态与预期不符,因此基于决策树算法设计了一种全新的线上教学资源分类管理系统。硬件部分设计了Intel Sandy bridge多核处理器及型号专用寄存器(Model Specific Register,MSR),软件部分基于策树分类节点计算了GiniIndex属性值,生成了教学资源属性分类选择策略,从而完成了线上教学资源分类管理。系统测试结果表明,基于决策树算法的线上教学资源分类管理系统的运行状态与预期相符,整体运行性能良好,符合线上教学资源的分类管理需求,有一定的应用价值,为教学资源的数字化管理作出了一定的贡献。
