Data mining in intelligent tutoring systems using rough sets

Data mining aims at searching for meaningful information like patterns and rules in large volumes of data Our objective is to mine the data of Intelligent Tutoring Systems (ITS). These are tutoring systems which offer the ability to respond to individualized student needs. An experiment was conducted over a lesson for binary relations. Students' answers to questions at the end of the lesson were collected Data mining was implemented to extract important rules from the data (students' answers) and hence the student can be directed to which parts of the lesson he should take again thus helping to adopt the tutoring systems to each student individual needs. Three approaches am applied to detect the decision rules based on the Rough Sets ad the Modified Rough Sets These approaches provide a powerful foundation to discover important structures in data Them approaches are unique in the sense that they only use the information given by the data and do not rely on other model assumptions. The results obtained were at the form of rules that showed what concepts, the student understood and which he did not understand depending on which questions he answered correct and which questions he answered wrong Also some questions of the quizzes were found to be useless. It was concluded that data mining was able to extract some important patterns and rules from tire students' answers which were hidden before ad which are helpful to both the students and the experts.