Modeling student performance to enhance the pedagogy of autoTutor
Abstract
The Tutoring Research Group from the University of Memphis has developed a pedagogically effective Intelligent Tutoring System (ITS), called AutoTutor, that implements conversational dialog as a tutoring strategy for conceptual physics. Latent Semantic Analysis (LSA) is used to evaluate the quality of student contributions and determine what dialog moves AutoTutor gives. By modeling the students' knowledge in this fashion, AutoTutor successfully adapted its pedagogy to match the ideal strategy for students' ability.
Publication Title
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Recommended Citation
Jackson, T., Mathews, E., Lin, K., Olney, A., & Graesser, A. (2003). Modeling student performance to enhance the pedagogy of autoTutor. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2702, 368-372. https://doi.org/10.1007/3-540-44963-9_50
