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Semantic networks from 900 high education students from different knowledge domains were obtained by using a computer system. Then computer simulated schemata behavior and/or analysis of semantic networks allowed to select relevant course schemata related words to implement semantic priming studies to test for students´ word recognition latencies of schemata word pairs before and after a course. A trained neural net successfully recognized students who integrated course schemata related concepts in their lexicon from those who did not by analyzing recognition times after a course. Thus, an innovative e-assessment was implemented by developing a software system that integrates cognitive reports of mental representation due to learning (constructive assessment) with cognitive reports of automatic recognition processing of course content (reactive assessment). It is argued that this combination of cognitive assessment leads to innovative advanced forms to evaluate e-learning.


e-assessment semantic networks neural nets reactive and constructive assessment.

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How to Cite
Morales-Martinez, G. E., & Lopez-Ramirez, E. O. (2016). Cognitive responsive e-assessment of constructive e-learning. Journal of E-Learning and Knowledge Society, 12(4).