Abstract: The student supervisors are facing a great challenge in Chinese universities that they have a lot of work to do and serve too many students directly, so that they can hardly give a personalized learning guide for every student. We propose a method of learning state analysis of students based on outlier detection to solve this problem and allocate the limited educational resources to the neediest students. This method finds the suspicious outlying students through mining the students’ scores based on the algorithm of density-based local outliers, and analyzes the learning state of these students. The case study shows that this method can efficiently find some students with exceptional learning state which may assist the college student supervisors in managing students more efficiently.

Key words: outlier detection, educational data mining, student's scores, learning state, local outlier factor; data mining