Difference between revisions of "Intelligent tutoring system"

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(Created page with "<u>Draft 1</u> '''Editor''': Nicolas Balacheff, Laboratoire d’informatique de Grenoble '''Contributors''': Jacqueline Bourdeau, Télé-université, Montréal (Québec); Moniq...")
 
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'''Editor''': Nicolas Balacheff, Laboratoire d’informatique de Grenoble
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'''Editor''': Jacqueline Bourdeau, Télé-université, Montréal<br>
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Monique Grandbastien, Université Henri-Poincaré, Nancy
  
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'''Contributors''': Jacqueline Bourdeau, Télé-université, Montréal (Québec); Monique Grandbastien, LORIA, université Henri Poincaré Nancy1
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'''Contributors''': …/…
  
 
====Definition====
 
====Definition====
  
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An Intelligent tutoring system (ITS) is a computer-based instructional systems with models of instructional content that specify what to teach, and teaching strategies that specify how to teach.
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An Intelligent tutoring system (ITS) is an AI-based system that can reason upon models of knowledge useful for fostering and evaluating learning. The main function of an ITS is to adapt to the learner through an understanding or an awareness of her cognitive, meta-cognitive or affective states.  
  
 
====Comments on the history====
 
====Comments on the history====
  
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The term “intelligent tutoring systems” was coined by David Sleeman and John Seely Brown acknowledging the evolution of the previously called Computer Assisted Instruction (CAI) into Intelligent Computer Assisted Instruction (ICAI), an expression that ITS replaced then (Sleeman and Brown 1982 p.1).  
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The term “intelligent tutoring systems” was coined by David Sleeman and John Seely Brown (Sleeman and Brown 1982 p.1), acknowledging the evolution of Computer Assisted Instruction (CAI) into Intelligent Computer Assisted Instruction (ICAI), and emphasizing the focus on individual learning.  In 1987, Wenger provided a detailed description of ITS in his seminal book entitled “Artificial Intelligence and Tutoring Systems”. In 1988 started the series of biannual ''ITS conferences''. Most results from ITS research are to be found in the ''International Journal of Artificial Intelligence and Education''.
  
 
====Related terms====
 
====Related terms====
  
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Computer Assisted Instruction (CAI), Intelligent Computer Assisted Instruction (ICAI), generative CAI, student model, learner model, knowledge model
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Student modeling; Student model; Learner modeling; Learner model; learner module; Knowledge representation; Knowledge model; Knowledge module; Pedagogical module; Educational data mining; Artificial Intelligence in Education.
  
 
====Translation issues====
 
====Translation issues====
  
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''French'': tuteur intelligent ; however, the best translation of “tutor” would have been “précepteur”, the word “tuteur” has in French a connotation close to authoritative instruction.
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French: système tutoriel intelligent; tuteur intelligent.
  
 
====Disciplinary issues====
 
====Disciplinary issues====
  
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ITS represents an important milestone in the structuration of research on AI and learning, which initially ambitioned covering all types of computer-supported teaching and learning. “ ITS has clearly abandoned on the CAI’s early objective, namely that of providing total courses, and has concentrated on building systems which provide supportive environments for limited topics” (Sleeman and Brown 1982, p.8).  A characteristic of ITS research is the emphasis on individualization and the requirement for the system to have its own problem solving expertise, both and their interaction requiring specific tutoring knowledge:  “Computer-assisted instruction evolves toward intelligent tutoring systems (ITSs) by passing three tests on intelligence. First, the subject matter, or domain, must be “known” to the computer system well enough for this embedded expert to draw inferences or solve problems in the domain. Second, the system must be able to deduce learners’ approximation of that knowledge. Third the tutorial strategy or pedagogy must be intelligent in that the “instructor in the box” can implement strategies to reduce the difference between expert and student performance.” (Burns and Capps, p.1). This is translated into the classical three modules architecture of ITSs: the domain model, the tutor model and the learner model. Following John Self (1999, p.350), basic architecture of ITSs was already established in the mid-seventies. However, “beside this classic component view of ITSs, these systems offer a number of services. The implementation of some of these services usually transcends the individual components” (Nkambou et al. 2010, p.5).
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The field of ITS is by nature interdisciplinary, at the crossroads of computer science (artificial intelligence, software engineering, data mining, HCI) and educational psychology, cognitive science and instructional science. ITS research challenges these fields both at the fundamental and the methodological levels, and stimulates interdisciplinary thinking. From its origin, it represents an important milestone in the structuration of research on AI and Learning.  Characteristics of ITS research is the emphasis on individualization and the requirement for the system to have its own problem solving expertise, as well as specific tutoring to conduct its interaction with the student:  “Computer-assisted instruction evolves toward intelligent tutoring systems (ITSs) by passing three tests on intelligence. First, the subject matter, or domain, must be “known” to the computer system well enough for this embedded expert to draw inferences or solve problems in the domain. Second, the system must be able to deduce learners’ approximation of that knowledge. Third the tutorial strategy or pedagogy must be intelligent in that the “instructor in the box” can implement strategies to reduce the difference between expert and student performance.” (Burns and Capps, p.1). This is translated into the classical three modules architecture of ITSs: the domain model, the tutor model and the learner model. Following John Self (1999, p.350), basic architecture of ITSs was already established in the mid-seventies. However, “beside this classic component view of ITSs, these systems offer a number of services. The implementation of some of these services usually transcends the individual components”, all of these aiming at fine-tuned adaptation to the learner (Nkambou et al. 2010, p.5).  
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====Key references====
 
====Key references====
  
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Burns and Capps (1988) Foundation of intelligent tutoring systems: an introduction. In: Polson M. C., Richardson J. J. (eds.) Intelligent tutoring systems (pp.1-19).Hillsdale, NJ: Laurence Erlbaum.
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Burns & Capps (1988) Foundation of intelligent tutoring systems: an introduction. In: Polson M. C., Richardson J. J. (eds.), Intelligent tutoring systems (pp.1-19).Hillsdale, NJ: Laurence Erlbaum.
  
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Nkambou R., Bourdeau J., Mizoguchi R. (eds.) (2010) Advances in Intelligent tutoring systems. Springer verlag.
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Nkambou R., Bourdeau J., Mizoguchi R. (eds.) (2010). Advances in Intelligent tutoring systems. Springer Verlag.
  
 
Self J. (1999) The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education, 10, 350-364
 
Self J. (1999) The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education, 10, 350-364
  
 
Sleeman D., Brown J. S. (eds) (1982) Intelligent tutoring systems. London: Academic Press.
 
Sleeman D., Brown J. S. (eds) (1982) Intelligent tutoring systems. London: Academic Press.
 +
 +
Wenger  E.  (1987) Artificial Intelligence and Tutoring Systems. Los Altos, CA:  Kaufman Publishers.
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Woolf B. (2009)  Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Burlington, MA, Morgan Kaufmann.

Revision as of 11:46, 10 February 2012

Draft 1

Editor: Jacqueline Bourdeau, Télé-université, Montréal
Monique Grandbastien, Université Henri-Poincaré, Nancy

Contributors: …/…

Definition

An Intelligent tutoring system (ITS) is an AI-based system that can reason upon models of knowledge useful for fostering and evaluating learning. The main function of an ITS is to adapt to the learner through an understanding or an awareness of her cognitive, meta-cognitive or affective states.

Comments on the history

The term “intelligent tutoring systems” was coined by David Sleeman and John Seely Brown (Sleeman and Brown 1982 p.1), acknowledging the evolution of Computer Assisted Instruction (CAI) into Intelligent Computer Assisted Instruction (ICAI), and emphasizing the focus on individual learning. In 1987, Wenger provided a detailed description of ITS in his seminal book entitled “Artificial Intelligence and Tutoring Systems”. In 1988 started the series of biannual ITS conferences. Most results from ITS research are to be found in the International Journal of Artificial Intelligence and Education.

Related terms

Student modeling; Student model; Learner modeling; Learner model; learner module; Knowledge representation; Knowledge model; Knowledge module; Pedagogical module; Educational data mining; Artificial Intelligence in Education.

Translation issues

French: système tutoriel intelligent; tuteur intelligent.

Disciplinary issues

The field of ITS is by nature interdisciplinary, at the crossroads of computer science (artificial intelligence, software engineering, data mining, HCI) and educational psychology, cognitive science and instructional science. ITS research challenges these fields both at the fundamental and the methodological levels, and stimulates interdisciplinary thinking. From its origin, it represents an important milestone in the structuration of research on AI and Learning. Characteristics of ITS research is the emphasis on individualization and the requirement for the system to have its own problem solving expertise, as well as specific tutoring to conduct its interaction with the student: “Computer-assisted instruction evolves toward intelligent tutoring systems (ITSs) by passing three tests on intelligence. First, the subject matter, or domain, must be “known” to the computer system well enough for this embedded expert to draw inferences or solve problems in the domain. Second, the system must be able to deduce learners’ approximation of that knowledge. Third the tutorial strategy or pedagogy must be intelligent in that the “instructor in the box” can implement strategies to reduce the difference between expert and student performance.” (Burns and Capps, p.1). This is translated into the classical three modules architecture of ITSs: the domain model, the tutor model and the learner model. Following John Self (1999, p.350), basic architecture of ITSs was already established in the mid-seventies. However, “beside this classic component view of ITSs, these systems offer a number of services. The implementation of some of these services usually transcends the individual components”, all of these aiming at fine-tuned adaptation to the learner (Nkambou et al. 2010, p.5).

Key references

Burns & Capps (1988) Foundation of intelligent tutoring systems: an introduction. In: Polson M. C., Richardson J. J. (eds.), Intelligent tutoring systems (pp.1-19).Hillsdale, NJ: Laurence Erlbaum.

Nkambou R., Bourdeau J., Mizoguchi R. (eds.) (2010). Advances in Intelligent tutoring systems. Springer Verlag.

Self J. (1999) The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education, 10, 350-364

Sleeman D., Brown J. S. (eds) (1982) Intelligent tutoring systems. London: Academic Press.

Wenger E. (1987) Artificial Intelligence and Tutoring Systems. Los Altos, CA: Kaufman Publishers.

Woolf B. (2009) Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Burlington, MA, Morgan Kaufmann.

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