Sub-symbolic knowledge extraction environment for teaching process assistance

We aim with this research is to build an integrated environment to serve as an assistant in the educational process. When we deal with unstructured knowledge, getting of useful information for the teaching process is very difficult. Neural networks can store subsymbolic knowledge, but until recently it was believed to be only in a "black-box" format. Knowledge extraction from NNs is a relatively new field, which tries to reduce these disadvantages and build a bridge between subsymbolic and symbolic knowledge. As the teaching process requires only symbolic knowledge, we believe this to be a chance for teachers to significantly improve their teaching materials and/or style by combining the symbolic knowledge of the domain theory with the rules extracted from the empirical subsymbolic knowledge stored in NNs trained on examples. Therefore, we developed a neural network's subsymbolic knowledge extraction environment for the teaching process assistance and also built a study case of teaching stock exchange developments.