A Study on the Possibility of Automatically Estimating the Confidence Value of Students' Knowledge in Generated Conceptual Models

We propose a new metric to automatically evaluate the confidence that a student knows a certain concept included in his or her conceptual model. The conceptual model is defined as a simplified representation of the concepts and relationships among them that a student keeps in his or her mind about an area of knowledge. Each area of knowledge comprises several topics and each topic several concepts. Each concept can be identified by a term that the students should use. A concept can belong to one topic or to several topics. Terms are automatically extracted from the answers provided to an automatic and adaptive free-text scoring system using Machine Learning techniques. In fact, the conceptual model is fully generated from the answers provided by the students to this system. In the paper, the automatic procedure that makes it possible is reviewed in detail. Finally, concept maps are used to graphically display the conceptual model to teachers and students. In this way, they can instantly see which concepts have already been assimilated and which ones should still be reviewed.