Acharya: An Intelligent Tutoring Environment for ... - C-DAC Mumbai

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useful in most cases. After the enhancement of student model and incorporation of aneffective question generator, we propose to release this on the web for general use.Designing an ITS is a complex task requiring attention to a number of aspects, andexpertise on a number of dimensions. Today there is, perhaps, no single ITS that providesgood solutions to all the different aspects such as adaptive instruction sequencing,individualized explanation, etc. Acharya also has focused on certain elements at the costof others. Some of these omitted elements are being now taken up. We describe theseextensions below:5.1 Enhancement of Student ModelAcharya uses a combination of overlay model and bug model to represent what has beenlearnt by the student. Student might even answer in a form different from one that expertgive. Acharya can identify some of these different strategies through its bug library.Another enhancement to student model is to build a model of the student reasoningprocess from the history of the answers supplied by student to the tutor, verifying itscorrectness and pointing out the errors.5.2 Generating Exercise ProblemsCurrently Acharya picks up exercise problems for practice from a manually createddatabase of problems stored along with the expert’s solution. For effective tutoring asystem must have a good repository of problems addressing all combinations of conceptcoverage and difficulty level. Manually creating this is not easy; so we are exploring thepossibility of automatically generating exercise problems.One major issue in generating exercise problem is to deal with the complexity of theproblem. In the current system where questions are pulled out from a database, acomplexity level is associated with each problem. The system selects a problem withappropriate complexity. In case of automatic question generation, the system has to makesure that a problem with appropriate complexity is getting created. Adaptability is animportant feature that an automatic question generation system should have. The studentshould get more questions on a specific topic, if he makes mistakes in that topic. IWe areimplementing an automatic question generation module, which generate questions andcorrect answers for them, based on templates defined for different topics.5.3 Generalising into a FrameworkAnother direction to pursue is to ask “is the Acharya model applicable (and usable) indeveloping other intelligent tutoring systems in other domains?” We feel it is prematuretoday to visualize a general purpose framework capable of supporting ITS across alldomains, e.g. Languages, mathematics, geography, programming skills, etc. The teachingmethodologies and domain representations may vary a lot. However, on the other hand itmakes little sense to approach each ITS as totally independent. One needs to identify ameaningful level of generality that is feasible and practically useful. We are attempting toevolve as general a framework as possible by extracting the parts specific to SQL teaching,and our specific methodology being followed.
AcknowledgementsThis work was carried out under the Vidyakash project of the Department of InformationTechnology, Ministry of Communications and Information Technology, Government ofIndia. We also thank the referees for their comments and suggestions on this paper.References[Anderson, 1988] Anderson J.R. Expert Module. In M.C. Polson and J.J. Richardson(Eds).Foundations of intelligent tutoring systems, Lawrence Erlbaum Associates, pp. 21-53,1988.[Brown and Vanlehn, 1980] Brown J.S. and Vanlehn K. Repair theory: A generative theoryof bugs in procedural skills. Cognitive science 4, pp. 379-426,1980.[Brown and Burton, 1975] Brown, J.S. and Burton R.R. Multiple representation ofknowledge for reasoning. In D. Bobrow 7 A. Collins (Eds), Representation andunderstanding: Studies in cognitive science, pp. 311-349, New York, Academic Press,1975.[Burton, 1982] Burton R.R. Diagnosing bugs in simple procedural skills. In Sleeman andBrown(Eds.). Intelligent tutoring systems, New York, Academic Press, pp. 157-184, 1982.[Carr and Goldstein, 1977] Carr, B. and Goldstein I. Overlays: A theory of modeling forcomputer aided instruction. M.I.T., AI-memo 406,1977.[Clancey, 1982] Clancey, W. J. Tutoring rules for guiding a case method dialogue. In D.Sleeman and J.S. Brown (Eds). Intelligent tutoring systems New York, Academic Press,pp. 201-225, 1982.[Ford, 1988] Ford, L. The appraisal of an ICAI system. In J. Self (Ed.). Atrificialintelligence and human learning, Chapman and Hall, pp. 109-123, 1988.[Halff, 1988] Halff, H.M. Curriculum and Instruction in Automated Tutors. In M.C. Polsonand J.J. Richardson (Eds). Foundations of intelligent tutoring systems, LawrenceErlbaum Associates, pp. 55-78, 1988.[Johnson and Soloway, 1984] Johnson, W.L. and Soloway E. PROUST: Knowledge-basedprogram debugging. Proceedings of the Seventh Iinternational Software EngineeringConference, pp. 369-380, 1984.[Mitrovic, 1997] Mitrovic, A. SQL-Tutor: A preliminary report. Technical report TRCOSC08/97. Computer science department. University of Canterbury, 1977.[Vanlehn, 1988] Vanlehn K. Student Modeling. In M.C. Polson and J.J. Richardson (Eds),Foundations of intelligent tutoring systems, Lawrence Erlbaum Associates, pp. 55-78,1988.
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