On the Use of Semantic-Based AIG to Automatically Generate Programming Exercises

Authors

Laura Zavala, CUNY Medgar Evers CollegeFollow
Benito Mendoza, CUNY New York City College of TechnologyFollow

Document Type

Article

Publication Date

2-2018

Abstract

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality.

Comments

This is an Accepted Manuscript of an article published by SIGCSE '18 Proceedings of the 49th ACM Technical Symposium on Computer Science Education available online: https://dl.acm.org/citation.cfm?id=3159608 DOI:10.1145/3159450.3159608