Intelligent adaptive testing system

Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive models in which complexity of tasks varies depending on correctness patient’s answers. This article describes development an intelligent system using a fuzzy mathematics device. An has been developed; module that implements expert uses production base rules. The input parameters percentage correct responses, degree response, duration and number attempts. output change current level training student basis test questions related selected. As method logical inference, Mamdani used consists six operational actions: phazification — conversion exact values variables into linguistic through belonging functions, this served as for designing rules system; aggregation sub-conditions determination truth conditions each rule inference activating sub-conclusions finding rule; accumulation conclusions function variables; defazzification numerical value variables. A developed (ISAT) presented allows, based analysis results, to determine students, adapt material their training. allows you dynamically present appropriate real time. When system, students will be provided with complexity, allow building individual trajectory. introduction predefined ensure implementation personalized approach organizing process; increase accuracy assessing students’ knowledge. use technology systems automated, control