Virtual teaching and learning environments: Automatic evaluation with symbolic regression

摘要

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 teaching and 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 teaching and learning environments, which offers the following improvements when compared to other methods: a) superior accuracy when compared with the linear regression method; b) simplicity of implementation; c) possible deduction of final student grades; and d) context adaptive. To this extent, a case study was applied to the LabSQL environment, with the purpose of clarifying the benefits of symbolic regression via genetic programming, while emphasizing its efficiency and simplicity of implementation.