FUDAOWANG: Implementing Advanced Education Concepts to Improve the Tutorial Intelligence of Intelligent Tutoring Systems

FUDAOWANG: Implementing Advanced Education Concepts to Improve the Tutorial Intelligence of Intelligent Tutoring Systems

Wei Xu (Xidian University, China), Ke Zhao (Xidian University, China), Yatao Li (Xidian University, China) and Zhenzhen Yi (Xidian University, China)
DOI: 10.4018/978-1-4666-6102-8.ch002
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The intelligence of an intelligent tutoring system is composed of reasoning intelligence and tutorial intelligence. The way to make an intelligent tutoring system with tutorial intelligence is to make sure the system has good tutoring functions. Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, which they call “FUDAOWANG.” The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective mature domain. Its characteristic is that the knowledge employed is the mature knowledge accepted by most people. FUDAOWANG uses automatic reasoning technology about objective mature problems to realize its reasoning intelligence. Based on the results of the automatic reasoning, FUDAOWANG synthetically applies the problem-based tutoring and the advanced education concepts to achieve tutoring functions of stepwise, prompt, detailed answers, rethinking after solution, consolidated exercise, etc. The evaluation of FUDAOWANG shows that it is helpful to the students in improving their learning achievements and cultivating good learning habits.
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With the increasing of science and technology, more and more technologies are applied in the field of education. A lot of education software that claimed to be able to help children study has appeared on the market. Does the education software can help students? What functions of good education software that teachers and students need should have?

Students in general need the software to solve difficult problems in their study or work with them to explore problems, rather than the software only providing some fixed contents of problem sets. Students who learn better wish the software could provide the environment of training their innovation abilities, instead of simply imparting knowledge. Teachers hope the software could also be powerful teaching assistants to help them answer the general questions raised by students, so that they have time and energy to do more creative work.

So far, many scholars and research institutions have made a big effort, and have developed many actual intelligent tutoring systems. On the basis of the summary that Shute and Psotka (1996) studied on the early intelligent teaching systems, this paper combines with the latest intelligent tutoring systems research, and summarizes the development process and the typical systems of intelligent tutoring systems, which is shown in Table 1.

Table 1.
The typical intelligent tutoring systems
ITSTimeDeveloperMain technologyReferences
SCHOLAR1970CarbonellProduction expert system(Carbonell, 1970)
WHY1977Stevens, CollinsStevens and Collins, 1977)
SOPHE1975Burton, BrownBrown and Burton, 1975)
WEST1976Burton, BrownBrown and Burton, 1976)
BUGGY1978Burton, BrownBrown and Burton, 1978)
GUIDON1979ClanceyClancey, 1979)
LISP Tutor1985Anderson, Boyle, ReiserCase-based reasoning,
Natural language understanding
(Anderson, Boyle, and Reiser, 1985)
Geometry Tutor1985Anderson, Boyle, YostAnderson, Boyle, and Yost, 1985)
PROUST1986JohnsonJohnson, 1986)
PIXIE1987Sleeman(Sleeman, 1987)
Smithtown1990Shute, GlaserIntelligent agent,
Natural language understanding,
Neural network
(Shute and Glaser, 1990)
Stat Lady1993Shute, Gawlick-Grendell(Shute and Gawlick-Grendell, 1993)
Sherlock1995Nichols, Pokorny, Jones, Gott, AlleyNichols, Pokorny, Jones, Gott, and Alley, 1995)
SQL-Tutor1996Mitrovic(Mitrovic, 1996)
Auto-Tutor1997Graesser(Graesser, 1997)
VC Prolog Tutor2000Peylo, Thelen, et alIntelligent agent,
Grid and distributed computing,
Natural language understanding
(Peylo, Thelen, Rollinger and Gust, 2000)
SCoT-DC2001Herbert, Clark, et alClark, Fry, Ginzton, Peters, Pon-Barry, and Thomsen-Gray, 2001)
Slide Tutor2003Crowley, MedvedevCrowley and Medvedeva, 2006)
AHP-Tutor2004Ishizaka, Lust(Ishizaka and Lust, 2004)
MATHEMA2009Papadimitriou, Grigoriadou, GyftodimosPapadimitriou, Grigoriadou, Gyftodimos, 2009)
Mathtutor2009Aleven, McLaren, SewallAleven, McLaren, Sewall, 2009)
IVRT2009Kim, WangKim and Wang, 2009)
Oscar CITS2012Latham, Crockett, Mclean,(Latham, Crockett, Mclean, 2012, 2014)

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