A majority-density approach to developing testing and diagnostic systems with the cooperation of multiple experts based on an enhanced concept-effect relationship model

Abstract

In the recent years, diagnosing students’ learning problems after testing and providing learning sugges- tions for them are an important research issue. Many studies have been conducted to develop a method for analyzing learning barriers of students such that helpful learning suggestions or guidance can be provided based on the analysis results. In this paper, we present a new procedure for integrating test item–concept relationship opinions based on majority density of multiple experts in order to enhance a concept–effect relationship model used for generating personalized feedback. It provides a useful and practical way to decrease inconsistencies in the weighting criteria of multiple experts and to enhance the entire learning-diagnosis procedure for developing testing and diagnostic systems.