Toward Non-intrusive Assessment in Dialogue-Based Intelligent Tutoring Systems

Author:

Rus Vasile,Stefanescu Dan

Publisher

Springer Singapore

Reference16 articles.

1. Chi, M. T. H., Siler, S. A., & Jeong, H. (2004). Can tutors monitor students’ understanding accurately? Cognition and Instruction, 22(3), 363–387.

2. Chi, M. T. H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.

3. Corcoran, T., Mosher, F. A., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. Consortium for Policy Research in Education Report #RR-63. Philadelphia, PA: Consortium for Policy Research in Education.

4. Forbes-Riley, K., & Litman, D. J. (2006). Modelling user satisfaction and student learning in a spoken dialogue tutoring system with generic, tutoring, and user affect parameters. In Proceedings of Human Language Technology Conference.

5. Lintean, M., Rus, V., & Azevedo, R. (2011). Automatic detection of student mental models during prior knowledge activation in metatutor. International Journal of Artificial Intelligence in Education, 21(3), 169–190.

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