Intelligent Tutoring Systems
Intelligent Tutoring Systems: From Wikipedia, the free encyclopedia
Intelligent tutoring systems (ITS) are computer systems that provide direct customized instruction or feedback to students, i.e. without the intervention of human beings, whilst performing a task. Thus, ITS implements the theory of learning by doing. 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. Intelligent Tutor Systems have been around since the late 1970s, but increased in popularity in the 1990s.
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.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 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 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.
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. Therefore a mere description is not enough; the knowledge contained in the models should be organized and linked to an inference engine. 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. 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 student learning outcomes on final exams and standardized tests. Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc. Intelligent Language Tutoring Systems (ILTS) is a subfiled of Intellignet Tutoring systems that is mainly concerned with teaching a natural language, producing error diagnosing and feedback of errors made by either first or second language learners. ILTS is complicated and require specialized natural language processing tools such large dictionaries, and morological and grammatical analyzers with acceptable coverage.
The Intelligent Tutoring Systems conference was typically held every other year in Montréal (Canada) by Claude Frasson and Gilles Gauthier in 1988, 1992, 1996 and 2000; in San Antonio (US) by Carol Redfield and Valerie Shute in 1998; in Biarritz (France) and San Sebastian (Spain) by Guy Gouardères and Stefano Cerri in 2002; in Maceio (Brazil) by Rosa Maria Vicari and Fábio Paraguaçu in 2004; in Jhongli (Taiwan) by Tak-Wai Chan in 2006. The conference was recently back in Montreal in 2008 (for its 20th anniversary) by Roger Nkambou and Susanne Lajoie. ITS’2010 was held in Pittsburgh (US) by Jack Mostow, Judy Kay, and Vincent Aleven. The International Artificial Intelligence in Education (AIED) Society (http://iaied.org) 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.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.
- Woolf, Beverly Park (2009). Building Intelligent Interactive Tutors. Morgan Kaufmann. ISBN 978-0-12-373594-2.
- Evens, Martha; Michael, Joel (2005). One-on-one Tutoring by Humans and Computers. Routledge. ISBN 9780805843606.
- Polson, Martha C.; Richardson, J. Jeffrey, eds (1988). Foundations of Intelligent Tutoring Systems. Lawrence Erlbaum. ISBN 0805800530.
- Psotka, Joseph; Massey, L. Dan; Mutter, Sharon, eds (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum. ISBN 0805800239.
- Wenger, Etienne (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann. ISBN 0934613265.
- Brown, D.; Sleeman, John Seely, eds (1982). Intelligent Tutoring Systems. Academic Press. ISBN 0126486808.
- Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology
- Intelligent Tutoring Systems: The What and the How
- Freedman, Reva (2000). “What is an Intelligent Tutoring System?”. Intelligence 11 (3): 15-16. doi:10.1145/350752.350756. http://www.cs.niu.edu/~freedman/papers/link2000.pdf.
- Intelligent Tutoring Systems: Using AI to Improve Training Performance and ROI
- A Framework for Model-Based Adaptive Training
- ^ Joseph Psotka, Sharon A. Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. ISBN 0805801928.
- ^ For an example of an ITS authoring tool, see Cognitive Tutoring Authoring Tools
- ^ Koedinger, K. R.; Corbett, A. (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
- ^ Shaalan K. An Intelligent Computer Assisted Language Learning System for Arabic Learners, Computer Assisted Language Learning: An International Journal, Taylor & Francis Group Ltd., 18(1 & 2): 81-108, February 2005.
- Intelligent Tutoring Systems in the AI topics website of the Association for the Advancement of Artificial Intelligence
- The 10th International Conference on Intelligent Tutoring Systems – Bridges to Learning – Pittsburgh (2010)
- The 9th International Conference on Intelligent Tutoring Systems – Intelligent Tutoring Systems: Past and Future – Montreal (2008)
- The 8th International Conference on Intelligent Tutoring Systems (2006)
- The 2007 Artificial Intelligence in Education conference.
- MERLOT – Multimedia Educational Resource for Learning and Online Teaching
- The Wolfram Demonstrations Project site
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