Learner knowledge level calculation by concept map and concept weight estimation using neural networks

Abstract

Nowadays we can observe fast expansion of the E-learning environment with an increase in the usage of internet and other technologies. In every educational environment, one of the most important challenges and issues is learner knowledge assessment. Because of this reason, it is necessary that an accurate method for learner assessment be implemented in every educational system. One of the newest tools that is used for teaching and assessment in schools and universities, is the concept map. The concept map is a graph that is used for representation and organization of knowledge in a special field. This graph contains concepts and meaningful relations between them. In this study, a new approach is proposed for the assessment of knowledge level by a concept map. In this approach, an expert person and student draw their concept map and then the score of the student is estimated in every concept in the map by comparing the student's map with the expert's map. Afterwards, a test of all of the concepts in the map is taken by the student and his/her total score is obtained. Finally, using the scores of each concept and total score, the weight or importance of each concept is calculated by neural networks.