Intelligent tutoring systems: a new proposed structure

Intelligent Tutoring Systems (ITS) could provide an excellent one-on-one support to improve students' conceptual understanding. The structure of a traditional ITS encompasses four modules: the knowledge module, the student module, the instructional module, and the presentation module. In our paper, that structure has been modified to improve system performance. The modifications that we added to the traditional structure were the Knowledge Manipulation Module and the Reporting Module. The reporting module is created to facilitate briefing each student's learning status to different instructors who can see the result of their pedagogical strategies as the system assesses and tutors each student. Accordingly, using the knowledge manipulation module the instructor can add, modify, delete, and edit any quiz question or lecture content. These two new introduced modules are expected to improve the performance of the intelligent tutoring systems and are considered to be one of the major contributions in the current proposed work. A case study is being implemented to show the impact of the designed system on students' understanding. Several sessions of professional development workshops are being planned for faculties who are interested in improving their students' understanding using the developed tool.

[1]  Brian P. Butz,et al.  An intelligent tutoring system for circuit analysis , 2006, IEEE Transactions on Education.

[2]  Zygmunt Scheidlinger.  Computers as Tutors: Solving the Crisis in Education , 1999, J. Educ. Technol. Soc..

[3]  Vincent Aleven,et al.  Scaling Up Programming by Demonstration for Intelligent Tutoring Systems Development: An Open-Access Web Site for Middle School Mathematics Learning , 2009, IEEE Transactions on Learning Technologies.

[4]  Cory J. Butz,et al.  A Web-Based Intelligent Tutoring System for Computer Programming , 2004, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04).

[5]  Meshref Hossam.  An Intelligent Tutoring System for Logic Circuit Design Problem Solving , 2011 .

[6]  Frederick Bennett.  Computers as Tutors: Solving the Crisis in Education , 1999 .

[7]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[8]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[9]  Kenneth H. Rosen,et al.  Discrete Mathematics and its applications , 2000 .

[10]  Jung-Fu Cheng,et al.  Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm , 1998, IEEE J. Sel. Areas Commun..

[11]  Cristina Conati,et al.  Intelligent Tutoring Systems: New Challenges and Directions , 2009, IJCAI.

[12]  Zita A. Vale,et al.  Training Control Centers' Operators in Incident Diagnosis and Power Restoration Using Intelligent Tutoring Systems , 2009, IEEE Transactions on Learning Technologies.

[13]  Arthur C. Graesser,et al.  AutoTutor: an intelligent tutoring system with mixed-initiative dialogue , 2005, IEEE Transactions on Education.

[14]  Peter L. Balise,et al.  Boolean Algebra and Its Applications , 1995 .

[15]  Neil T. Heffernan,et al.  Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System , 2009, IEEE Transactions on Learning Technologies.

[16]  Ana L. N. Fred,et al.  Designing Intelligent Tutoring Systems: A Bayesian Approach , 2001, ICEIS.

[17]  George D. Magoulas,et al.  Neural network-based fuzzy modeling of the student in intelligent tutoring systems , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[18]  Kenneth H. Rosen.  Discrete Mathematics and Its Applications: And Its Applications , 2006 .

[19]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .