Download Artificial Intelligence in Education: Supporting Learning by C.-K. Looi, G. McCalla, B. Bredeweg, J. Breuker PDF

By C.-K. Looi, G. McCalla, B. Bredeweg, J. Breuker

The sector of man-made Intelligence in schooling has endured to expand and now contains learn and researchers from many components of know-how and social technology. This learn opens possibilities for the cross-fertilization of knowledge and concepts from researchers within the many fields that make up this interdisciplinary learn region, together with man made intelligence, different parts of desktop technological know-how, cognitive technology, schooling, studying sciences, academic know-how, psychology, philosophy, sociology, anthropology, linguistics, and the numerous domain-specific components for which man made Intelligence in schooling structures were designed and outfitted. An specific objective is to entice these researchers who proportion the point of view that actual development in studying expertise calls for either deep perception into expertise and in addition deep perception into freshmen, studying, and the context of studying. The subject displays this uncomplicated duality.

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Table 3. Correlation between Test Scores and REDEEM use Pre-test scores Pre-test score Semester 1 score Semester 2 score No. of lectures Semester 1 score Semester 2 score No. 287*** Note. 001 (two tailed test of significance) A stepwise linear regression predicted the influence of REDEEM use and Semester 1 performance on Semester 2 performance. 7% of the variance (adjusted R squared). 001). 001) but both were significant predictors. 954%) better for each REDEEM lecture they completed. These analyses suggest that REDEEM improves students’ performance, but it is still possible to argue that those students who used REDEEM more frequently were harder working and motivated students.

The tutor also provides a second form of help, a searchable on-line Glossary with detailed information about the relevant geometry theorems and definitions, which students can browse freely. The tutor keeps track of the student’s knowledge growth over time, using a Bayesian algorithm to estimate students’ mastery of the skills targeted in the instruction (Corbett & Anderson, 1995). The Cognitive Tutors uses these estimates to select problems, while the Help Tutor uses them to determine the amount of help a student may need on any given step.

P. Suraweera and A. Mitrovic, "An Intelligent Tutoring System for Entity Relationship Modeling," International Journal of Artificial Intelligence in Education, vol. 14, pp. 375-417, 2004. K. VanLehn, C. Lynch, L. Taylor, A. Weinstein, R. Shelgy, K. Schulze, D. Treacy, and M. Wintersgill, "Minimally invasive tutoring of complex physics problem solving," in Proceedings of the 6th International Conference ITS 2002, vol. 2363, S. A. Cerri, G. Gouardères, and F. Paraguaçu, Eds. Berlin: Springer-Verlag, 2002, pp.

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