Adapting instruction in search of 'a significant difference'
Journal of Network and Computer Applications
Learning styles and performance in the introductory programming sequence
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
Cognitive tutors as modeling tools and instructional models
Smart machines in education
Incorporating Learning Characteristics into an Intelligent Tutor
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
iWeaver: towards 'learning style'-based e-learning in computer science education
ACE '03 Proceedings of the fifth Australasian conference on Computing education - Volume 20
A design-first curriculum for teaching Java in a CS1 course
ACM SIGCSE Bulletin
Student modeling with atomic bayesian networks
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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To approximate more closely effective human tutors, intelligent tutoring systems should adapt not only to a student's knowledge but also her learning style. We introduce a pedagogical framework that incorporates the Felder-Silverman learning style model and validated instrument for assessing individual learning style. The framework provides a feedback infrastructure based on the learning style model dimensions (such as visual, verbal, intuitive, sensor, etc.). It has been implemented as part of the DesignFirst-ITS, helping novices learn how to design a class in UML from a problem description. The system has been evaluated with high-school students and results show that learning style based feedback helps students realize higher learning gains.