Decades of research suggest that students learn more effectively through one-on-one human tutoring than through any other known method of instruction. Seminal studies have shown that one-on-one human tutoring is significantly more effective than group instruction and may provide unparalleled opportunities for affective scaffolding to support affective states conducive to learning. Intelligent tutoring systems research aims to create intelligent systems that equal or surpass the effectiveness of human tutors, and a particularly promising approach involves natural language tutoring systems which engage students in rich dialogue. While great strides have been made, current natural language tutoring systems do not approach the strategic robustness and flexibility of human tutors. Many exciting challenges are posed by tutorial dialogue systems research. These challenges stem from the need to understand student’s natural language statements, recognize student’s goals and plans, and generate effective tutorial support, all while balancing the cognitive and affective considerations that impact student learning.

The JavaTutor project aims to understand how expert tutors provide effective cognitive and affective scaffolding over the course of long-term tutorial interactions to improve learning. The project builds on the extensive study of cognitive scaffolding within the intelligent tutoring systems community and leverages an increasingly active body of research on the role of affect in designing intelligent tutoring systems.

Introductory computing offers a particularly rich domain for tutorial dialogue research because in the U.S. alone, a significant shortfall of computing professionals is projected in coming years. It is critically important to increase the number of students who obtain degrees in computing. At the postsecondary level, creating effective introductory computing courses is essential to recruit and retain students, and JavaTutor focuses on computing education for this student population.

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