New Approaches to E-Cognitive Assessment of E-Learning

A technology transfer project to innovate ecognitive assessment is described. Specifically, natural semantic networks (as opposed to idiosyncratic or artificial semantic nets) from 880 high school and bachelor students and teachers from different knowledge domains were used to computer simulate schemata behavior regarding each domain. Schemata-related words were implemented in a semantic priming study that students had to take before and after a course. Then a neural net was implemented capable of discriminating between successful and unsuccessful students by analyzing students recognition times to schemata-related words. A computer system was developed to use these research results to empower teachers with reports of schema development, meaning formation indexes and neural net classification due to learning. It is argued that this project should point to new alternatives assess e-learning. The idea is not to substitute current standard assessment but to use computer science advances to innovate assessment of e-learning.