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An intelligent tutoring system (ITS) is any computer system that provides direct customized instruction or feedback to students, i. A computer is a Machine that manipulates data according to a list of instructions. e. without the intervention of human beings, whilst performing a task. [1] Thus, ITS implements the theory of learning by doing. Learning-by-doing is a Concept of Economic theory. It refers to the capability of workers to improve their productivity by regularly repeating the same type of action An ITS may employ a range of different technologies. However, usually such systems are more narrowly conceived of as artificial intelligence systems, more specifically expert systems made to simulate aspects of a human tutor. An expert system is Software that attempts to reproduce the performance of one or more human Experts most commonly in a specific Problem domain, and is Intelligent Tutor Systems have been around since the late 1970s, but increased in popularity in the 1990s.

Contents

The structure of an ITS system

Intelligent tutoring systems consist of four different subsystems or modules: the interface module, the expert module, the student module, and the tutor module. The interface module provides the means for the student to interact with the ITS, usually through a graphical user interface and sometimes through a rich simulation of the task domain the student is learning (e. Simulation is the imitation of some real thing state of affairs or process g. , controlling a power plant or performing a medical operation). The expert module references an expert or domain model containing a description of the knowledge or behaviors that represent expertise in the subject-matter domain the ITS is teaching -- often an expert system or cognitive model. An expert system is Software that attempts to reproduce the performance of one or more human Experts most commonly in a specific Problem domain, and is A cognitive model is an approximation to animal cognitive processes (predominantly human for the purposes of comprehension and prediction An example would be the kind of diagnostic and subsequent corrective actions an expert technician takes when confronted with a malfunctioning thermostat. The student module uses a student model containing descriptions of student knowledge or behaviors, including his or her misconceptions and knowledge gaps. An apprentice technician might, for instance, believe a thermostat also signals too high temperatures to a furnace (misconception) or might not know about thermostats that also gauge the outdoor temperature (knowledge gap). A mismatch between a student's behavior or knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction. To be able to do this, it needs information about what a human tutor in such situations would do: the tutor model. In British Australian New Zealand Italian and some Canadian universities, a tutor is often but not always a Postgraduate Student or a Lecturer

An intelligent tutoring system is only as effective as the various models it relies on to adequately model expert, student and tutor knowledge and behavior. Thus, building an ITS needs careful preparation in terms of describing the knowledge and possible behaviors of experts, students and tutors. This description needs to be done in a formal language in order that the ITS may process the information and draw inferences in order to generate feedback or instruction. A formal language is a set of words, ie finite strings of letters, or symbols. Therefore a mere description is not enough, the knowledge contained in the models should be organized and linked to an inference engine. In Computer science, and specifically the branches of Knowledge engineering and Artificial intelligence, an inference engine is a Computer program It is through the latter's interaction with the descriptive data that tutorial feedback is generated.

Use in practice

All this is a substantial amount of work, even if authoring tools have become available to ease the task[2]. This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, Cognitive Tutors, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved study learning outcomes on final exams and standardized tests[3]. A cognitive tutor is an Intelligent tutoring system which develops a Cognitive model of a Student as he or she interacts with the program providing problems Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc.

ITS conference

The Intelligent Tutoring Systems conference was typically held every other year in Montréal at the Université de Montréal in Canada by Claude Frasson in 1998, 1992 1996 and 2000 and will again be in 2008. The ITS conference was held in San Antonio in 1998, Brazil in 2004, and Taiwan in 2006. The International Artificial Intelligence in Education (AIED) Society (http://aied.inf.ed.ac.uk/aiedsoc.html) publishes The International Journal of Artificial Intelligence in Education (IJAIED) and produces the International Conference on Artificial Intelligence in Education every odd numbered year. The American Association of Artificial Intelligence (AAAI)(www. The Association for the Advancement of Artificial Intelligence or AAAI is an international nonprofit scientific society devoted to advancing the scientific understanding of aaai. org) sometimes has symposia and papers related to intelligent tutoring systems. A number of books have been written on ITS including three published by Lawrence Erlbaum Associates.

See also

Bibliography

Books

Papers

References

  1. ^ Joseph Psotka, Sharon A. A learning object is a resource usually digital and web-based that can be used and re-used to support learning Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. ISBN 0805801928.  
  2. ^ For an example of an ITS authoring tool, see Cognitive Tutoring Authoring Tools
  3. ^ Koedinger, K. R. & Corbett, A. Kenneth R Koedinger (born 1962 in Wisconsin) is a professor of Human-computer interaction and Psychology at Carnegie Mellon University. (2006), “Cognitive Tutors: Technology bringing learning science to the classroom”, in Sawyer, K. , The Cambridge Handbook of the Learning Sciences, Cambridge University Press, pp. 61-78 

External links

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