Auto diagnosing: An intelligent assessment system based on Bayesian Networks
Liang Zhang, Yueting Zhuang, Zhenming Yuan, Guohua Zhan · 2007
In recent years, e-learning system has become more and more popular and many effective assessment systems have been proposed to offer students for convenience of their self-assessment. However, conventional test systems simply provide students a score, and do not provide adaptive learning guidance for students. Thus, how to automatically diagnose student's learning status and provide learning help becomes an interesting issue. This study proposes an assessment model based on Bayesian Networks, which assesses learning status by knowledge map after absorbing and analyzing test results. In order to form adaptive and tailored feedback, rule inference and exact inference are applied to combine Knowledge map with teacher's experience rules. Experimental results have demonstrated that the novel model benefits students and deserves further investigation.