A majority-density approach to developing testing and diagnostic systems with the cooperation of multiple experts based on an enhanced concepteffect relationship model
In the recent years, diagnosing students learning problems after testing and providing learning suggestions 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 itemconcept relationship opinions based on majority density of multiple experts in order to enhance a concepteffect 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.
concepteffect relationship model computer-based testing computer-assisted learning diagnostic learning system multi-expert system