Chapter 5 Development of Knowledge Based Intelligent Tutoring System

Intelligent Tutoring Systems (ITS) are computer-based tutors which act as a supplement to human teachers. The major advantage of an ITS is it can provide personalized instructions to students according to their cognitive abilities. The classical model of ITS architecture has three main modules – domain, model, student model and teaching model. In this chapter, we have discussed the recent developments in those areas and tried to address the important issues involved with them. We have developed an ITS. We have used two major data structures (a tree and a directed graph) to represent the domain knowledge within the domain model. The domain model also consists of a repository which is a collection of study and test materials. The contents in the repository are annotated using various informative tags for intelligent retrieval of them for the students. A fuzzy state based student model is used to represent a student’s cognitive ability and learning behavior. This is an important task as the adaptation techniques to be followed by the system are dependent to the student’s ability. The actual adaptation techniques are planned and executed by the control engine, which is the teaching model of the system. The control engine communicates with the domain and the student model and takes decisions using fuzzy rules. We have evaluated our ITS and found quite encouraging results.

[1]  Tom Murray,et al.  Authoring Intelligent Tutoring Systems: An analysis of the state of the art , 1999 .

[2]  Joseph E. Beck,et al.  Modeling the Student with Reinforcement Learning , 1997 .

[3]  Vincent Aleven,et al.  The Cognitive Tutor Authoring Tools (CTAT): Preliminary Evaluation of Efficiency Gains , 2006, Intelligent Tutoring Systems.

[4]  Joseph E. Beck,et al.  High-Level Student Modeling with Machine Learning , 2000, Intelligent Tutoring Systems.

[5]  Jim E. Greer,et al.  Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets , 1996, Intelligent Tutoring Systems.

[6]  Huaiqing Wang,et al.  Intelligent student profiling with fuzzy models , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[7]  Raymond J. Mooney,et al.  Refinement-based student modeling and automated bug library construction , 1996 .

[8]  Ramzan Khuwaja,et al.  Architecture of CIRCSIM-Tutor (v.3): a smart cardiovascular physiology tutor , 1994, Proceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS).

[9]  Kurt VanLehn,et al.  Andes: A Coached Problem Solving Environment for Physics , 2000, Intelligent Tutoring Systems.

[10]  Timothy Wang,et al.  Using neural networks to predict student's performance , 2002, International Conference on Computers in Education, 2002. Proceedings..

[11]  Chi-Jen Lin,et al.  Redefining the learning companion: the past, present, and future of educational agents , 2003, Comput. Educ..

[12]  Neil T. Heffernan,et al.  Student Modeling in an Intelligent Tutoring System , 2011 .

[13]  Jim Reye A Belief Net Backbone for Student Modelling , 1996, Intelligent Tutoring Systems.

[14]  Antonija Mitrovic,et al.  On Using Learning Curves to Evaluate ITS , 2005, AIED.

[15]  Jirí Vomlel,et al.  Bayesian Networks In Educational Testing , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[16]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

[17]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[18]  Jim Reye Two-Phase Updating of Student Models Based on Dynamic Belief Networks , 1998, Intelligent Tutoring Systems.

[19]  David Rosenthal,et al.  An Assessment of Constraint-Based Tutors: A Response to Mitrovic and Ohlsson's Critique of "A Comparison of Model-Tracing and Constraint-Based Intelligent Tutoring Paradigms" , 2006, Int. J. Artif. Intell. Educ..

[20]  Albert T. Corbett,et al.  Intelligent Tutoring Systems , 1985, Science.

[21]  Joel D. Martin,et al.  J. Evaluation on an assessment system based on Bayesian student modeling , 1997 .

[22]  Julita Vassileva,et al.  Course sequencing techniques for large-scale web-based education , 2003 .

[23]  Scotty D. Craig,et al.  Integrating Affect Sensors in an Intelligent Tutoring System , 2004 .

[24]  J. Cid-Sueiro,et al.  Student modeling based on fuzzy inference mechanisms , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[25]  Martha W. Evens,et al.  A practical student model in an intelligent tutoring system , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[26]  H. Pain,et al.  ' Did I say what I think I said , and do you agree with me ? ' : Inspecting and Questioning the Student Model , 1995 .

[27]  Judith D. Wilson,et al.  Artificial Intelligence and Tutoring Systems , 1990 .

[28]  B. Bloom The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring , 1984 .

[29]  Cristina Conati,et al.  On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks , 1997 .

[30]  Antonija Mitrovic,et al.  An Intelligent SQL Tutor on the Web , 2003, Int. J. Artif. Intell. Educ..

[31]  Jinil Kim,et al.  Learning achievement evaluation strategy using fuzzy membership function , 2001, 31st Annual Frontiers in Education Conference. Impact on Engineering and Science Education. Conference Proceedings (Cat. No.01CH37193).

[32]  Martha W. Evens,et al.  CIRCSIM-Tutor: An Intelligent Tutoring System Using Natural Language Dialogue , 1997, ANLP.

[33]  Michael Villano,et al.  Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory , 1992, Intelligent Tutoring Systems.

[34]  Reva Freedman Plan-Based Dialogue Management in a Physics Tutor , 2000, ANLP.

[35]  Beverly Park Woolf,et al.  Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels , 2006, Intelligent Tutoring Systems.

[36]  Jim E. Greer,et al.  Interacting with Inspectable Bayesian Student Models , 2004, Int. J. Artif. Intell. Educ..

[37]  J. S. Brown,et al.  Pedagogical, natural language, and knowledge engineering techniques in SOPHIE-I, II and III , 1982 .

[38]  Mir Sadique Ali A Neuro Fuzzy Inference System For Student Modeling In Web-Based Intelligent Tutoring Systems , 2004 .

[39]  Geoffrey I. Webb,et al.  Comparative evaluation of alternative induction engines for Feature Based Modelling , 1997 .