Abstract
The present research focuses on the development of an intelligent, computer-based tutoring model for selecting problems in domains where multiple skills are needed to solve a problem and the reasons for errors are not easily diagnosed. In this paper we report on the development and evaluation of the Mental Rotation Tutors and the intelligent models driving the problem selection engine or “domain reasoner”. The domain reasoner evaluated each student based upon seven different core skills and chose the next problem based upon the student's level of proficiency in all seven areas. Two versions of the tutor were developed. The first versions targeted improving the student's ability to infer what combination of rotations were required to go from one view to another. The second version targeted improving the student's ability to apply a provided set of rotations to an object and report the final orientation. The results of two successive experiments demonstrated that students with low spatial ability derived the most measurable benefit from interacting with the tutors. The tutors also successfully diagnosed students' skill levels and provided problems that were appropriate to each student's current level of proficiency.
Get full access to this article
View all access options for this article.