Self-organizing maps
A practical procedure to build a knowledge structure
Journal of Mathematical Psychology
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Automata for the Assessment of Knowledge
IEEE Transactions on Knowledge and Data Engineering
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Applying Demand Analysis of a Set of Test Problems for Developing Adaptive Courses
ICCE '02 Proceedings of the International Conference on Computers in Education
The New Review of Hypermedia and Multimedia - Hypermedia and the world wide web
SmartTutor: an intelligent tutoring system in web-based adult education
Journal of Systems and Software
Learning path generation by domain ontology transformation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
A map-based visualization tool to support tutors in e-learning 2.0
HSI'09 Proceedings of the 2nd conference on Human System Interactions
ICHL'10 Proceedings of the Third international conference on Hybrid learning
A semantic analysis approach for assessing professionalism using free-form text entered online
Computers in Human Behavior
A framework for designing closed domain virtual assistants
Expert Systems with Applications: An International Journal
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A novel assessment procedure based on knowledge space theory (KST) is presented along with a complete implementation of an intelligent tutoring system (ITS) that has been used to test our theoretical findings. The key idea is that correct assessment of the student knowledge is strictly related to the structure of the domain ontology. Suitable relationships between the concepts must be present to allow the creation of a reverse path from the "knowledge state" representing the student goal to the one that contains her actual knowledge about this topic. Knowledge space theory is a very good framework to guide the process of building the ontology used by the artificial tutor. The system we present uses a conversational agent to assess the student knowledge through a natural language question/answer procedure. The system exploits a Cyc-based common sense ontology about the specific domain of interest to select the concepts needed to explain unknown topics emerging from the dialogue. Besides, the latent semantic analysis (LSA) technique is used to determine the correctness of the student sentences in order to establish which concepts she knows. As a result, the system supplies learning material arranged as a path between the unknown topics resulting from the student assessment. The learning path is presented to the student by a user-friendly graphical interface, which allows to access documents browsing a visual map. The procedure is explained in detail along with the rest of the system, and the assessment validation results are presented.