An Explanation Tool to Support Learning of Basic Programming

Intelligent tutoring systems (ITS) have assisted engineering students in several domains. The domains considered ideal for ITS contain easily represented issues in computational form and allow the interaction type between student and ITS be limited to a restricted set of words, symbols, and numbers. It is proposed to exploit intelligent system technology to support an explanation process in the context of ITS. A system was developed to support explanations of examples to assist the learning process of basic programming. Examples of C programs, previously elaborated by a teacher, are presented to a student from who are expected explanations to source-code regions. Using techniques of approximate natural language understanding, the system tries to recognize explanation contents to send the result to a module that classifies explanations as correct, incorrect, or incomplete according to the context of the proposed activity. The context can be configured by the teacher. After explanation processing, an ITS could determine the subsequent stages according to its educational strategy

[1]  Michael Redmond,et al.  Encouraging Self-Explanation Through Case-Based Tutoring: A Case Study , 1997, ICCBR.

[2]  John R. Anderson,et al.  Student modeling in the ACT Programming Tutor. , 1995 .

[3]  Vincent Aleven,et al.  Towards Tutorial Dialog to Support Self- Explanation: Adding Natural Language Understanding to a Cognitive Tutor * , 2001 .

[4]  Susan McRoy,et al.  What is an Intelligent Tutoring System , 2000 .

[5]  Michelene T. H. Chi,et al.  Eliciting Self-Explanations Improves Understanding , 1994, Cogn. Sci..

[6]  Arthur C. Graesser,et al.  AutoTutor: A simulation of a human tutor , 1999, Cognitive Systems Research.

[7]  K. VanLehn,et al.  Teaching Meta-cognitive Skills: Implementation and Evaluation of a Tutoring System to Guide Self- Explanation While Learning from Examples , 1999 .

[8]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[9]  Gerhard Weber,et al.  ELM-PE: A Knowledge-based Programming Environment for Learning LISP. , 1994 .

[10]  Kurt VanLehn,et al.  DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions , 2000, Intelligent Tutoring Systems.

[11]  Peter Brusilovsky,et al.  ELM-ART: An Intelligent Tutoring System on World Wide Web , 1996, Intelligent Tutoring Systems.

[12]  Reva Freedman,et al.  Delivering Hints in a Dialogue-Based Intelligent Tutoring System , 1999, AAAI/IAAI.

[13]  K. Koedinger,et al.  A Knowledge-Based Approach to Understanding Students ’ Explanations , 2003 .

[14]  Silke Schworm,et al.  Learning by Solved Example Problems: Instructional Explanations Reduce Self-Explanation Activity , 2002 .

[15]  Cristina Conati,et al.  A computer framework to support self-explanation* , 1998 .

[16]  Matthew W. Lewis,et al.  Self-Explonations: How Students Study and Use Examples in Learning to Solve Problems , 1989, Cogn. Sci..

[17]  Alan M. Lesgold.  Intelligent Tutoring Systems , 1992 .

[18]  Susan McRoy,et al.  Links: what is an intelligent tutoring system? , 2000, INTL.

[19]  Arthur C. Graesser,et al.  Approximate Natural Language Understanding for an Intelligent Tutor , 1999, FLAIRS Conference.

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

[21]  Albert T. Corbett,et al.  Student Modeling in the ACT Programming Tutor: Adjusting a Procedural Learning Model With Declarative Knowledge , 1997 .