Knowledge-Based System in an Affective and Intelligent Tutoring System

2017 ◽  
pp. 95-113 ◽  
Author(s):  
Ramón Zatarain Cabada ◽  
María Lucía Barrón Estrada ◽  
Yasmín Hernández Pérez
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Knowledge Based System ◽  
Knowledge Based

Research on Knowledge-Based Intelligent Tutoring System

2011 ◽  
Vol 55-57 ◽  
pp. 1424-1429
Author(s):  
Zhen Zhen Yi ◽  
Ke Zhao ◽  
Ya Tao Li ◽  
Wei Xu
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Agent Technology ◽  
Tutoring System ◽  
Language Understanding ◽  
Automatic Reasoning ◽  
Resource Modeling ◽  
Class A ◽  
Knowledge Based

Aiming at students’ learning process, and based on the analysis of tutorship rules of students’ learning after class, a Knowledge-Based Intelligent Tutoring System is given. The system comprehensively uses agent technology, the knowledge-based automatic reasoning, resource modeling for knowledge classification, field natural language understanding, data mining, computer networks, databases and other technologies. It creates a student-oriented self-motivated learning environment in which students can learn abundant knowledge of one or many subjects, send the problems encountered in their own learning to the system server by network, and get real-time multiple tutorship information as an excellent teacher do.

scholarly journals Modeling Human-Like Robustness in an Intelligent Tutoring System

1970 ◽  
Vol 3 (1) ◽  
pp. 2-8
Author(s):  
Hameedullah Kazi ◽  
Peter Haddawy ◽  
Siriwan Suebnukarn
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Specific Solution ◽  
Knowledge Based ◽  
Narrow Scope

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few human-like attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.

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