Analysing student programs in the PHP intelligent tutoring system

Weragama, Dinesha & Reye, Jim (2014) Analysing student programs in the PHP intelligent tutoring system. International Journal of Artificial Intelligence in Education, 24(2), pp. 162-188.

View at publisher

Abstract

Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that supports students learning to program. The PHP ITS works by providing exercises for students to solve and then providing feedback based on their solutions. The major challenge here is to be able to identify many semantically equivalent solutions to a single exercise. The PHP ITS achieves this by using theories of Artificial Intelligence (AI) including first-order predicate logic and classical and hierarchical planning to model the subject matter taught by the system. This paper highlights the approach taken by the PHP ITS to analyse students’ programs that include a number of program constructs that are used by beginners of web development. The PHP ITS was built using this model and evaluated in a unit at the Queensland University of Technology. The results showed that it was capable of correctly analysing over 96 % of the solutions to exercises supplied by students.

Impact and interest:

3 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 84032
Item Type: Journal Article
Refereed: Yes
Keywords: PHP, Intelligent Tutoring System, Knowledge base, Program analysis, HERN
DOI: 10.1007/s40593-014-0014-z
ISSN: 1560-4292
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 International Artificial Intelligence in Education Society
Deposited On: 11 May 2015 22:29
Last Modified: 12 May 2015 22:09

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page