How did the e-learning session go? The Student Inspector
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Flexible Environment for Supervising Simulation-Based Learning Situations
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Plan recognition in exploratory domains
Artificial Intelligence
Automatic recognition of learner groups in exploratory learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Plan recognition in virtual laboratories
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
Exploratory Learning Environments (ELE) are open-ended and flexible software, supporting interaction styles that include exogenous actions and trial-and-error. This paper shows that using AI techniques to visualize worked examples in ELEs improves students' generalization of mathematical concepts across problems, as measured by their performance. Students were exposed to a worked example of a problem solution using an ELE for statistics education. One group in the study was presented with a hierarchical plan of relevant activities that emphasized the sub-goals and the structure relating to the solution. This visualization used an AI algorithm to match a log of activities in the ELEs to ideal solutions. We measured students' performance when using the ELE to solve new problems that required generalization of concepts introduced in the example solution. The results showed that students who were shown the plan visualization significantly outperformed other students who were presented with a step-by-step list of actions in the software used to generate the same solution to the example problem. Analysis of students' explanations of the problem solution shows that the students in the former condition also demonstrated deeper understanding of the solution process. These results demonstrate the benefit to students when using AI technology to visualize worked examples in ELEs and suggests future applications of this approach to actively support students' learning and teachers' understanding of students' activities.