Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Making large class teaching more adaptive with the logic-ITA
ACE '04 Proceedings of the Sixth Australasian Conference on Computing Education - Volume 30
Turning automata theory into a hands-on course
Proceedings of the 37th SIGCSE technical symposium on Computer science education
The Logic-ITA in the Classroom: A Medium Scale Experiment
International Journal of Artificial Intelligence in Education
Software Patterns in ITS Architectures
International Journal of Artificial Intelligence in Education
Usage analysis in learning systems
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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Web-based learning tools are often introduced in addition to face-to-face teaching as an extra resource for students to train themselves outside the classroom. How can teachers know whether and how these tools represent an added value for students? In this paper we argue that usage data stored by such tools help teachers to get insight on the learning impact of those tools and that this must be taken into account at the design phase. This is illustrated with two web-based tools Logic-ITA and JFLAP.