知识点学习的智能评价

Can Students Grasp Study Points?: An Intelligent Evaluation System

测试是整个教学行为中的一个重要环节,也是评价教学效果、教学质量的主要途径。众多学者对经典测试理论(CTT)和项目反应理论(IRT)等测试理论应用的研究,却对测试后个人的评价问题却被淡忘。这并不能满足测试的本质目标。测试评价是整个教学行为中的一个重要环节,对于评价教学、评价学生甚至人才培养至关重要。本文是计算智能在测试评价中应用的研究,首先介绍了利用隶属度函数的量化方式来刻画学习者的认知程度,较详细地介绍了模糊评判优化知识点测试评价的演算过程,具体地说是利用模糊评判进行知识点的测试评价,以确定是否完成某个知识点的学习以及学习者对该知识点的认知程度等。将计算智能引入到测试评价中,给出某知识点的通过阀值,为实现E-learning中的知识点内容呈现自适应和导航自适应服务,这对于形成更人性化、个性化的智能测试评价具有十分重要的理论意义。

Testing is a key element in teaching and an important means to assess teaching outcomes and quality. Many applied studies have focused on Classical Testing Theory and Item Response Theory but few have examined what has happened after testing has been conducted. This neglect of post-testing analysis is unsatisfactory as this analysis is essential for both learning and teaching. The current paper consists of applied research on an intelligent evaluation system. First, levels of understanding are described quantitatively using co-relational functions. Then, the algorithm process of fuzzy evaluation regarding different study points is discussed. Finally, fuzzy evaluation is applied to examine whether and to what extent students have grasped particular study points. In the process, a threshold value is generated for different study points, so students can become autonomous learners in the e-learning process.