Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
An Intelligent SQL Tutor on the Web
International Journal of Artificial Intelligence in Education
Using Learning Decomposition to Analyze Instructional Effectiveness in the ASSISTment System
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Using contextual factors analysis to explain transfer of least common multiple skills
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Learning what works in ITS from non-traditional randomized controlled trial data
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Learning what works in its from non-traditional randomized controlled trial data
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Content learning analysis using the moment-by-moment learning detector
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Researchers that make tutoring systems would like to know which pieces of educational content are most effective at promoting learning among their students. Randomized controlled experiments are often used to determine which content produces more learning in an ITS. While these experiments are powerful they are often very costly to setup and run. The majority of data collected in many ITS systems consist of answers to a finite set of questions of a given skill often presented in a random sequence. We propose a Bayesian method to detect which questions produce the most learning in this random sequence of data. We confine our analysis to random sequences with four questions. A student simulation study was run to investigate the validity of the method and boundaries on what learning probability differences could be reliably detected with various numbers of users. Finally, real tutor data from random sequence problem sets was analyzed. Results of the simulation data analysis showed that the method reported high reliability in its choice of the best learning question in 89 of the 160 simulation experiments with seven experiments where an incorrect conclusion was reported as reliable (p