A proposal for automatic evaluation by symbolic regression in virtual learning environments

Empirically, symbolic regression tries to identify, through genetic programming and within the sphere of mathematical expressions, a model which best explains the relationship between variables in a given set of data, in terms of precision and simplicity. Virtual learning environments focused on evaluation have been previously investigated, as they offer teachers an effective teaching and learning tool and the student the possibility of computer-assisted evaluation and customized learning. Within this context, the present paper introduces an alternative approach to automatic evaluation in virtual learning environments, which offers the following improvements when compared to other methods, as superior accuracy when compared with the linear regression method, simplicity of implementation and context adaptive. To this extent, it presents the benefits of symbolic regression through genetic programming, emphasizing its efficiency and simplicity of implementation.