Self organized schemata behavior and meaning formation to evaluate E-learning

This paper presents a comprehensive method to implement e-assessment using computer simulated schemata behavior that is based on natural semantic networks obtained from students before and after an academic course. Specifically, a computer system capable of obtaining natural semantic nets from students (as opposed to idiosyncratic semantic nets designed by a researcher) empower implementation of a constrains satisfaction neural net capable of emulate schemata behavior that can be used to formative e-assessment of learning. Empirical support to this approach to evaluate students long term retention of knowledge is presented trough a set of semantic priming studies considering different knowledge domains. Implications to innovate the XXI digital classroom are discussed.