大学英语诊断性练习系统中诊断性评价模型研究  

Diagnostic Evaluation Model of College English Diagnostic Test System

作　　者：吴涛 张晖 吴敏 WU Tao, ZHANG Hui, WU Min(Center of Modem Educational Technology, University of Science and Technology of China, Hefei 230026, China)

机构地区：[1]中国科学技术大学现代教育技术中心,合肥230026

出　　处：《计算机系统应用》2018年第11期90-95,共6页Computer Systems & Applications

摘　　要：在线学习系统中没有教师对学习者学习状态进行评价,大多需要学习者自主设定学习策略、调整学习步伐,这导致了学习者缺乏个性化的指导,使得部分学习者的学习效率不高.为了解决这个问题,本文在大学英语诊断性练习系统中提出了诊断性评价模型.该模型使用大量实际数据,从学习状态、题型关联分析、知识点关联分析和四级成绩预测这四个角度进行数据分析,并使用S-P表分析法、数据挖掘和机器学习等分别建模,最终将这四个模型结合得到了诊断性评价模型,并在大学英语诊断性练习系统中进行实现.实验结果表明,诊断性评价模型可以有效辅助学习者进行练习,提高四级成绩.There are no teachers to evaluate the learning state of the learners in the online learning system, so most of the learners need to set their own learning strategies and adjust their learning steps, which lead to the lack of individualized guidance for the learners and the poor efficiency of the learners. In order to solve this problem, a diagnostic evaluation model was proposed in the College English Diagnostic Test System. The model analyzed the data collected in the database from the four aspects of learners’ learning condition, question types association analysis, knowledge point association analysis, and CET-4 grade prediction, and it used Student-Problem Chart analysis method, data mining, and machine learning to build models respectively. Finally, the diagnostic evaluation model was obtained by merging these four sub models and it was implanted to improve the College English Diagnostic Test System. The experimental results showed that the diagnostic evaluation can effectively help learners to practice and improve their grades of CET-4.

关 键 词：诊断性评价模型 S-P表 数据挖掘 关联分析 机器学习 

分 类 号：TP317.2[自动化与计算机技术—计算机软件与理论]