Measuring the Quality of Assessment Using Questions Generated from the Semantic Web

This article describes a new feature of the adaptive assessment system SIETTE that allows for the static and dynamic generation of questions from tables of data for knowledge assessment. Almost the same approach can be used to generate questions from data collected in a spreadsheet, a database query, or a semantic web query using SPARQL. The main problem faced with question generation is ensuring that the questions are valid for assessment. For this reason, most of the existing systems propose to use this mechanism only for low-stakes assessments. In this paper, we propose a methodology to control question generation quality and measure the impact of potential invalid instances on the final score as well as recommend some strategies to overcome these problems.