Extracting learning concepts from educational texts in intelligent tutoring systems automatically

This paper argues that the educational support systems can give a meaning to an educational content semantically, and an answer has been sought to the question of “what should be taught to students” in the field of intelligent tutoring systems. With reference this aim, a system, which automatically detects the concepts to be learned by students, has been designed. The developed system uses the statistical language models together with conceptual map modeling as a student model to extract the minimal set of learning concepts within an educational content. study, ten corpora have been generated as a learning domain, which consist of two different subjects in mathematics. For each subject, five distinct chapters have been quoted from the books written by various authors. After extracting the candidate concepts from the given content, the system checks to clarify whether these candidates are in a dictionary within postprocessing. The dictionary consists of approximately 9500 technical terms related to the learning domain. The system performance has also been analyzed using Recall, Precision and F-measure scores. The results indicate that the postprocessing step increases precision with a small loss of recall.