Typifying Students’ Help-Seeking Behavior in an Intelligent Tutoring System for Mathematics

Abstract

The use of tutoring systems has become normalized in secondary schools (grades 7-9) in many parts of the world. There have been studies analyzing the students' behavior, their affective responses, or the abuse of the system, but little has been done to discover other types of behavior. This paper presents evidence that there are different types of help-seeking behavior which can be typified in Mexican students interacting with the Scooter intelligent tutoring system (ITS), which was designed to teach mathematics at secondary-level. The implemented methodology consisted of applying discovery algorithms and data mining to typify students in terms of their help-seeking behaviors. The results and contributions of this work suggest that gaming the system with the aforementioned ITS may not always be useful. Future work will analyze other student groups who have used this software in other parts of the world to correlate these typologies to students' traits or opinions about mathematics and learning.

Keywords

• educational technology
• educational innovation
• mining sequence data
• intelligent tutoring system
• behavioral education.