AToM3: A Tool for Multi-formalism and Meta-modelling
FASE '02 Proceedings of the 5th International Conference on Fundamental Approaches to Software Engineering
Issues in the Practical Use of Graph Rewriting
Selected papers from the 5th International Workshop on Graph Gramars and Their Application to Computer Science
Constraint-Based Tutors: A Success Story
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
KERMIT: A Constraint-Based Tutor for Database Modeling
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Automated Advice-Giving Strategies for Scientific Inquiry
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Journal of Network and Computer Applications
An Intelligent Tutoring System for Entity Relationship Modelling
International Journal of Artificial Intelligence in Education
A comparative analysis of cognitive tutoring and constraint-based modeling
UM'03 Proceedings of the 9th international conference on User modeling
A framework for process-solution analysis in collaborative learning environments
International Journal of Human-Computer Studies
Model-driven assessment of learners in open-ended learning environments
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Education and Information Technologies
Hi-index | 0.00 |
Inquiry learning is a didactic approach in which students acquire knowledge and skills through processes of theory building and experimentation. Computer modeling and simulation can play a prominent role within this approach. Students construct representations of physical systems using modeling. Using simulation, they execute these representations to study the phenomena or systems modeled. However, the modeling task is complex, and students can fail to create adequate models, which prevents effective learning. This necessitates supportive measures to scaffold the modeling processes. In this paper, we address the issue of designing such support through the development of intelligent advice to be incorporated in modeling environments. The advice is based on the definition of a family of reference solutions for each modeling problem, on the comparison of the reference solutions with the students' solutions, and on the use of an advice knowledge base. This advice guides the students to the construction of a better solution, helping them acquire the knowledge required for successful modeling and for the correction of modeling mistakes. In a collaborative session, having the advice encourages discussion between students about the advice and the best way of proceeding. Empirical validations of the advice approach are presented.