RFBR grant 20-07-00764 (2020-2021): Conceptual modeling of the knowledge domain on the comprehension level for intelligent decision-making systems in the learning

CompPrehension (Computer Prehension, but also Free Prehension) is a developing question-answer system that should be able to generate questions on comprehension level, find correct answers, grade students' answers, and generate explanations using automatic reasons. Thus, the logical model for the reasoner will capture the comprehension of the subject domain, and the reasoner (i.e. artificial intelligence) will comprehend it.

The system will support different subject domains as plugins as they must contain not just concepts and logical rules, but also task generators and explanation generators.

The project aims at supporting different reasoners as backends, but ontological reasoner Pellet is our first choice.

The first subject domain to research is Programming Basics. People wanting to develop this or other domains please contact the Project Lead, Oleg Sychev.

Preliminary Results

Control Structures Ontology - aims at matching control-structure tree with program traces and finding errors in trace building, developing comprehension of control structures. Repository: https://github.com/den1s0v/c_owl

Expression Ontology - aims at building evaluation trees and grading order-of-execution for the operators, developing comprehension of concepts like precedence and associativity. Repository: https://github.com/ShadowGorn/ontology_cpp_parsing