Modeling Knowledge Assessment Systems within a Hybrid Intelligent Learning Environment

Abstract

An approach to the intellectual assessment of students' knowledge within the framework of a hybrid intelligent learning environment (HILE) based on the construction and training of neural networks is considered. Structural schemes of the system for assessing knowledge in mathematics for schoolchildren of various grades are developed. The models of the pedagogical process "teacher" – "HILE module" – "student", "teacher" – "student", as well as a neural network agent model of the learning process are considered. The choice of types of neural networks and types of machine learning is substantiated, taking into account goal-setting. Neural network algorithms and training criteria for neural networks are characterized. The results obtained are aimed at creating methods that provide the processes of operational learning, control and assessment of knowledge, competencies and procedures, the level of formation of subject and professional competencies of students in the information and educational intellectual environment.

Keywords

• hybrid intelligent learning environment
• neural network algorithms
• dynamic systems
• machine learning
• knowledge base
• mathematical education
• artificial intelligence
• innovations in education