Analyzing learning concepts in intelligent tutoring systems

The information that is increasing and changing rapidly at the present day, and the usage of computers in educational and instructional processes has become inevitable. With the rapid progress in technology, research gives more importance to integrate intelligent issues with educational support systems such as distance learning and learning management systems. Such studies are considered as applications of the artificial intelligence on educational processes. Regarding this viewpoint, some supervised learning models which is able to recognize the learning concepts from a given educational content presented to a tutoring system has been designed, in this study. For this aim, firstly, three different corpora constructed from educational contents related to the subject titles such as calculus, abstract algebra and computer science have been composed. For each candidate learning concepts, the feature vectors have been generated using a relation factor in addition to tf-idf values. The relation factor is defined as the ratio of the total number of the most frequent substrings in the corpus that appear with a candidate concept in the same sentence within an educational content to most frequent substring in the corpus. The achievement of this system is measured according to the F-measure.