Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain

Authors:
Roger Nkambou;Engelbert Mephu Nguifo;Philippe Fournier-Viger
Affiliations:
Laboratoire GDAC, Université du Québec à Montréal, Montréal, Canada;Université Lille-Nord de France, Artois, F-62307 Lens, CRIL, F-62307 Lens, CNRS UMR 8188, Lens, France F-62307;Laboratoire GDAC, Université du Québec à Montréal, Montréal, Canada
Venue:
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Year:
2008

Citing 9
Cited 5

SPADE: an efficient algorithm for mining frequent sequences

Machine Learning
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach

IEEE Transactions on Knowledge and Data Engineering
Benchmarking the effectiveness of sequential pattern mining methods

Data & Knowledge Engineering
A comparative analysis of cognitive tutoring and constraint-based modeling

UM'03 Proceedings of the 9th international conference on User modeling
IGB: a new informative generic base of association rules

PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains

ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
An approach to intelligent training on a robotic simulator using an innovative path-planner

ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems

A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Experimental evaluation of automatic hint generation for a logic tutor

AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Enhancing the automatic generation of hints with expert seeding

ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Enhancing the automatic generation of hints with expert seeding

International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Experimental Evaluation of Automatic Hint Generation for a Logic Tutor

International Journal of Artificial Intelligence in Education - Best of AIED 2011

Quantified Score

Hi-index	0.01

Visualization

Abstract

Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users' solutions in an ill-defined procedural domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.