Enabling fine granularity of difficulty ranking measure for automatic quiz generation

Abstract

This Automatic Quiz Generation system is utterly handy for reducing teachers' workloads in quiz creation. Nevertheless, by exploiting a coarse-granular concern inside difficulty ranking mechanism, only a few number of automaticgenerated quizzes can be obtained. In order to increase the number of usable quizzes, we suggest how a 5-level difficulty ranking score using a hybrid similarity measurement approach together with property filtering of the key data can be potential for serving this propose. Based on experiment results, our proposed similarity measure outperforms three other candidates. Enabling users with finer options of making sensible quiz generation. Hence, this mechanism can be regarded as a synergistic technology for improving teachers' quality of life for the future.